I want to post current news & related information from the financial world, related to stock news.

Show me enough support and interest so I can effort this hobby of mine into a regular habit:

Finance market terminology
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Nifty50 shows the index may be on course to test the 10,000 mark in 2017…

Visit this Page for More Latest news N update.


seeing red

Posted: January 5, 2012 in headlines.

wow.. can’t belv..-anything is possible..


The Virtual Laser Keyboard leverages the power of laser and infrared technology and projects a full-size keyboard onto any flat surface. As you type on the laser projection, detection technology based on optical recognition enables the user to tap the images of the keys, complete with realistic tapping sounds, which feed into the compatible Bluetooth-enabled PDA, Smartphone, laptop or PC. 

The light weight device weighs two ounces and is similar in size to a disposable cigarette lighter. The Virtual Laser Keyboard includes a self-contained, rechargeable lithium ion battery. It provides the Virtual Laser Keyboard with its own internal power supply, so it doesn’t drain any battery power from the PDA or PC. The battery lasts two to three hours

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awesome application.. android itself -s awesome os..

Random Thoughts

So in the New Year if you’ve purchased an Android-powered phone, here’s what you must do next. After all we carry our phones literally everywhere and use it innumerable times throughout the day, so why not help the phone look betterBest Android Apps and also better its performance? Based on my experience with Android Phones over the past 18 months, I have collated a set of apps that can help you in transforming your phone and make your Android experience better, more productive, and more fun. As the title says, all of them are free to download and just requires your time and bandwidth.

1. Go Launcher Ex – One of the best things about Android is being able to customize everything about your home screen. “Launchers” or “home screen replacements” apps allow you to customize the way the screen looks when you press the Home button on your phone. While there are…

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i was curious enough to know abt these….

Kirkland's Blog


Siri has no doubt changed everything that people think of when it comes to voice recognition.  Though so far as down-to-earth functionality, it really doesn’t do too much that other phones have been doing since the beginning.  All the innovation is located in how you can say whatever you want to Siri in any form that you want, and it will complete the task.  Now if others (Google and Microsoft) intend to compete with Siri, I recommend that they take the next step above what Apple has started at.

This I believe begins with Microsoft creating a server farm that is basically a “Cloud Watson”.  And given that IBM and Microsoft have a historically cooperative past, I believe this can be accomplished.  For those of you who don’t know, IBM’s Watson was a computer program running on IBM Power 750 servers that competed against, and very well I might add…

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Purana saal sabse ho raha hai door,
Kya karein yahi hai kudrat ka dastoor,
Purane yaadein sochkar udaas na ho tum,
Naya saal aaya hai chalo…


contract has been renewed for the Year 2012.
So, try to be more LOVING and CARING next year.

Take care of Me and Miss me.
Because, It’s impossible to find a FRIEND
Who is
95% ideal
96% smart
97% kind
98% true
99% Talented
100% lovable
Wish you a Very Happy New Year


There have been many time in 2011
when I may disturbed you
troubled u
irritated u
bugged u
today I just wanna tell you
I plan to continue it in 2012.

Project Titles for Computer engineering

Here I have posted some 50 topics which you can choose for your final year project from Data Mining ,If you are not get what you’re looking for,do leave us your comment at below box, we’ll try to add more contents ,and project related all materials .
Share this site with your friends and continue your support to keep NewProjectIdeasLive…

Data Mining Based Projects:-


1. Building a Multiple-Criteria Negotiation Support System

2. An Exploratory Study of Database Integration Processes

3. COFI approach for Mining Frequent Item sets

4. Online Random Shuffling of Large Database Tables

5. A Flexible Content Adaptation System Using a Rule-Based Approach

6. Efficient Revalidation of XML Documents

7. Practical Algorithms and Lower Bounds for Similarity Search in Massive Graphs

8. Enhancing the Effectiveness of Clustering with Spectra Analysis

9. Efficient Monitoring Algorithm for Fast News Alerts

10. Top-k Monitoring in Wireless Sensor Networks

11. Frequent Closed Sequence Mining without Candidate Maintenance

12. Maintaining Strong Cache Consistency for the Domain Name System coal-illo-0807

13. Efficient Skyline and Top-k Retrieval in Subspaces

14. Efficient Process of Top-k Range-Sum Queries over Multiple Streams with Minimized Global Error

15. Fast Nearest Neighbor Condensation for Large Data Sets Classification

16. Wildcard Search in Structured Peer-to-Peer Networks

17. Neural-Based Learning Classifier Systems

18. Discovering Frequent Agreement Sub trees from Phylogenetic Data

19. Watermarking Relational Databases Using Optimization-Based Techniques

20. Extracting Actionable Knowledge from Decision Trees

21. A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems

22. The Threshold Algorithm: From Middleware Systems to the Relational Engine

23. Rank Aggregation for Automatic Schema Matching

24. Rule Extraction from Support Vector Machines: A Sequential Covering Approach

25. Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions

26. A Note on Linear Time Algorithms for Maximum Error Histograms

27. Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification FIGUR 4354

28. An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data

29. A Method for Estimating the Precision of Place name Matching

30. Efficiently Querying Large XML Data Repositories: A Survey

31. Graph-Based Analysis of Human Transfer Learning Using a Game Tested

32. Evaluating Universal Quantification in XML

33. Customer Profiling & Segmentation using Data Mining Techniques

34. Efficient Frequent Item set Mining Using Global Profit Weighted (GPW) Support Threshold

35. Fast Algorithms for Frequent Item set Mining using FP-Trees

36. Mining Confident Rules without Support Requirement

37. Mining Frequent Item set without Support Threshold

38. unified framework for utility based measures

39. An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval

40. The Google Similarity Distance

41. Reverse Nearest Neighbors Search in Ad Hoc Subspaces

42. Quality-Aware Sampling and Its Applications in Incremental Data Mining

43. An Exact Data Mining Method for Finding Center Strings and All Their Instances

44. Negative Samples Analysis in Relevance Feedback

45. Bayesian Networks for Knowledge-Based Authentication

46. Continuous Nearest Neighbor Queries over Sliding Windows

47. The Concentration of Fractional Distances

48. Efficient Approximate Query Processing in Peer-to-Peer Networks

49. Ontology-Based Service Representation and Selection

50. Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets.

imagesComputerIf U want some more »DATA-figures-diagram« then just follow/click the link below:-

KNOW the project is completedd!!

AirplaneNOW the project is completed!! its ur turn to link the broken!!..Thumbs up

SchoolTO know MORE, just check PRIVIOUS post..

Quote of the Day:
Adversity attracts the man of character. He seeks out the bitter joy of responsibility.
Charles de Gaulle


An Introduction to Data Mining

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.

Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, “Which clients are most likely to respond to my next promotional mailing, and why?”

This white paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today’s business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.

The Foundations of Data Mining

Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:

  • Massive data collection
  • Powerful multiprocessor computers
  • Data mining algorithms

Commercial databases are growing at unprecedented rates. A recent META Group survey of data warehouse projects found that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel multiprocessor computer technology. Data mining algorithms embody techniques that have existed for at least 10 years, but have only recently been implemented as mature, reliable, understandable tools that consistently outperform older statistical methods.

In the evolution from business data to business information, each new step has built upon the previous one. For example, dynamic data access is critical for drill-through in data navigation applications, and the ability to store large databases is critical to data mining. From the user’s point of view, the four steps listed in Table 1 were revolutionary because they allowed new business questions to be answered accurately and quickly.

Evolutionary Step Business Question Enabling Technologies Product Providers Characteristics
Data Collection(1960s) “What was my total revenue in the last five years?” Computers, tapes, disks IBM, CDC Retrospective, static data delivery
Data Access(1980s) “What were unit sales in New England last March?” Relational databases (RDBMS), Structured Query Language (SQL), ODBC Oracle, Sybase, Informix, IBM, Microsoft Retrospective, dynamic data delivery at record level
Data Warehousing&Decision Support(1990s) “What were unit sales in New England last March? Drill down to Boston.” On-line analytic processing (OLAP), multidimensional databases, data warehouses Pilot, Comshare, Arbor, Cognos, Microstrategy Retrospective, dynamic data delivery at multiple levels
Data Mining(Emerging Today) “What’s likely to happen to Boston unit sales next month? Why?” Advanced algorithms, multiprocessor computers, massive databases Pilot, Lockheed, IBM, SGI, numerous startups (nascent industry) Prospective, proactive information delivery

Table 1. Steps in the Evolution of Data Mining.

The core components of data mining technology have been under development for decades, in research areas such as statistics, artificial intelligence, and machine learning. Today, the maturity of these techniques, coupled with high-performance relational database engines and broad data integration efforts, make these technologies practical for current data warehouse environments.

The Scope of Data Mining

Data mining derives its name from the similarities between searching for valuable business information in a large database — for example, finding linked products in gigabytes of store scanner data — and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

  • Automated prediction of trends and behaviors. Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data — quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
  • Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand complex data. High speed makes it practical for users to analyze huge quantities of data. Larger databases, in turn, yield improved predictions.

Databases can be larger in both depth and breadth:

  • More columns. Analysts must often limit the number of variables they examine when doing hands-on analysis due to time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High performance data mining allows users to explore the full depth of a database, without preselecting a subset of variables.
  • More rows. Larger samples yield lower estimation errors and variance, and allow users to make inferences about small but important segments of a population.

A recent Gartner Group Advanced Technology Research Note listed data mining and artificial intelligence at the top of the five key technology areas that “will clearly have a major impact across a wide range of industries within the next 3 to 5 years.”2 Gartner also listed parallel architectures and data mining as two of the top 10 new technologies in which companies will invest during the next 5 years. According to a recent Gartner HPC Research Note, “With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast stores of detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage (0.9 probability).”3

The most commonly used techniques in data mining are:

  • Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
  • Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) .
  • Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution.
  • Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbor technique.
  • Rule induction: The extraction of useful if-then rules from data based on statistical significance.

Many of these technologies have been in use for more than a decade in specialized analysis tools that work with relatively small volumes of data. These capabilities are now evolving to integrate directly with industry-standard data warehouse and OLAP platforms. The appendix to this white paper provides a glossary of data mining terms.

How Data Mining Works

How exactly is data mining able to tell you important things that you didn’t know or what is going to happen next? The technique that is used to perform these feats in data mining is called modeling. Modeling is simply the act of building a model in one situation where you know the answer and then applying it to another situation that you don’t. For instance, if you were looking for a sunken Spanish galleon on the high seas the first thing you might do is to research the times when Spanish treasure had been found by others in the past. You might note that these ships often tend to be found off the coast of Bermuda and that there are certain characteristics to the ocean currents, and certain routes that have likely been taken by the ship’s captains in that era. You note these similarities and build a model that includes the characteristics that are common to the locations of these sunken treasures. With these models in hand you sail off looking for treasure where your model indicates it most likely might be given a similar situation in the past. Hopefully, if you’ve got a good model, you find your treasure.

This act of model building is thus something that people have been doing for a long time, certainly before the advent of computers or data mining technology. What happens on computers, however, is not much different than the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the computer must run through that data and distill the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don’t know the answer. For example, say that you are the director of marketing for a telecommunications company and you’d like to acquire some ne w long distance phone customers. You could just randomly go out and mail coupons to the general population – just as you could randomly sail the seas looking for sunken treasure. In neither case would you achieve the results you desired and of course you have the opportunity to do much better than random – you could use your business experience stored in your database to build a model.

As the marketing director you have access to a lot of information about all of your customers: their age, sex, credit history and long distance calling usage. The good news is that you also have a lot of information about your prospective customers: their age, sex, credit history etc. Your problem is that you don’t know the long distance calling usage of these prospects (since they are most likely now customers of your competition). You’d like to concentrate on those prospects who have large amounts of long distance usage. You can accomplish this by building a model. Table 2 illustrates the data used for building a model for new customer prospecting in a data warehouse.FIGUR34

Customers Prospects
General information (e.g. demographic data) Known Known
Proprietary information (e.g. customer transactions) Known Target

Table 2 – Data Mining for Prospecting

The goal in prospecting is to make some calculated guesses about the information in the lower right hand quadrant based on the model that we build going from Customer General Information to Customer Proprietary Information. For instance, a simple model for a telecommunications company might be:

98% of my customers who make more than $60,000/year spend more than $80/month on long distance

This model could then be applied to the prospect data to try to tell something about the proprietary information that this telecommunications company does not currently have access to. With this model in hand new customers can be selectively targeted.

Test marketing is an excellent source of data for this kind of modeling. Mining the results of a test market representing a broad but relatively small sample of prospects can provide a foundation for identifying good prospects in the overall market. Table 3 shows another common scenario for building models: predict what is going to happ en in the future.

Yesterday Today Tomorrow
Static information and current plans (e.g. demographic data, marketing plans) Known Known Known
Dynamic information (e.g. customer transactions) Known Known Target

Table 3 – Data Mining for Predictions

If someone told you that he had a model that could predict customer usage how would you know if he really had a good model? The first thing you might try would be to ask him to apply his model to your customer base – where you already knew the answer. With data mining, the best way to accomplish this is by setting aside some of your data in a vault to isolate it from the mining process. Once the mining is complete, the results can be tested against the data held in the vault to confirm the model’s validity. If the model works, its observations should hold for the vaulted data.

An Architecture for Data Mining

To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the organization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on. Figure 1 illustrates an architecture for advanced analysis in a large data warehouse.

Figure 1 – Integrated Data Mining Architecture

The ideal starting point is a data warehouse containing a combination of internal data tracking all customer contact coupled with external market data about competitor activity. Background information on potential customers also provides an excellent basis for prospecting. This warehouse can be implemented in a variety of relational database systems: Sybase, Oracle, Redbrick, and so on, and should be optimized for flexible and fast data access.

An OLAP (On-Line Analytical Processing) server enables a more sophisticated end-user business model to be applied when navigating the data warehouse. The multidimensional structures allow the user to analyze the data as they want to view their business – summarizing by product line, region, and other key perspectives of their business. The Data Mining Server must be integrated with the data warehouse and the OLAP server to embed ROI-focused business analysis directly into this infrastructure. An advanced, process-centric metadata template defines the data mining objectives for specific business issues like campaign management, prospecting, and promotion optimization. Integration with the data warehouse enables operational decisions to be directly implemented and tracked. As the warehouse grows with new decisions and results, the organization can continually mine the best practices and apply them to future decisions.

This design represents a fundamental shift from conventional decision support systems. Rather than simply delivering data to the end user through query and reporting software, the Advanced Analysis Server applies users’ business models directly to the warehouse and returns a proactive analysis of the most relevant information. These results enhance the metadata in the OLAP Server by providing a dynamic metadata layer that represents a distilled view of the data. Reporting, visualization, and other analysis tools can then be applied to plan future actions and confirm the impact of those plans.

Profitable Applications

A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on).

Some successful application areas include:

  • A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months. The data needs to include competitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a wide-area network that enables the representatives to review the recommendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the organization to be applied in specific sales situations.
  • A credit card company can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be identified. Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing campaigns over conventional approaches.
  • A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.
  • A large consumer package goods company can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and competitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments.

Each of these examples have a clear common ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These organizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them.


Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. However, there is a growing gap between more powerful storage and retrieval systems and the users’ ability to effectively analyze and act on the information they contain. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. A new technological leap is needed to structure and prioritize information for specific end-user problems. The data mining tools can make this leap. Quantifiable business benefits have been proven through the integration of data mining with current information systems, and n ew products are on the horizon that will bring this integration to an even wider audience of users.

  1. META Group Application Development Strategies: “Data Mining for Data Warehouses: Uncovering Hidden Patterns.”, 7/13/95 .
  2. Gartner Group Advanced Technologies and Applications Research Note, 2/1/95.
  3. Gartner Group High Performance Computing Research Note, 1/31/95.

Glossary of Data Mining Terms

analytical model A structure and process for analyzing a dataset. For example, a decision tree is a model for the classification of a dataset.
anomalous data Data that result from errors (for example, data entry keying errors) or that represent unusual events. Anomalous data should be examined carefully because it may carry important information.
artificial neural networks Non-linear predictive models that learn through training and resemble biological neural networks in structure.
CART Classification and Regression Trees. A decision tree technique used for classification of a dataset. Provides a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. Segments a dataset by creating 2-way splits. Requires less data preparation than CHAID.
CHAID Chi Square Automatic Interaction Detection. A decision tree technique used for classification of a dataset. Provides a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. Segments a dataset by using chi square tests to create multi-way splits. Preceded, and requires more data preparation than, CART.
classification The process of dividing a dataset into mutually exclusive groups such that the members of each group are as “close” as possible to one another, and different groups are as “far” as possible from one another, where distance is measured with respect to specific variable(s) you are trying to predict. For example, a typical classification problem is to divide a database of companies into groups that are as homogeneous as possible with respect to a creditworthiness variable with values “Good” and “Bad.”
clustering The process of dividing a dataset into mutually exclusive groups such that the members of each group are as “close” as possible to one another, and different groups are as “far” as possible from one another, where distance is measured with respect to all available variables.
data cleansing The process of ensuring that all values in a dataset are consistent and correctly recorded.
data mining The extraction of hidden predictive information from large databases.
data navigation The process of viewing different dimensions, slices, and levels of detail of a multidimensional database. See OLAP.
data visualization The visual interpretation of complex relationships in multidimensional data.
data warehouse A system for storing and delivering massive quantities of data.
decision tree A tree-shaped structure that represents a set of decisions. These decisions generate rules for the classification of a dataset. See CART and CHAID.
dimension In a flat or relational database, each field in a record represents a dimension. In a multidimensional database, a dimension is a set of similar entities; for example, a multidimensional sales database might include the dimensions Product, Time, and City.
exploratory data analysis The use of graphical and descriptive statistical techniques to learn about the structure of a dataset.
genetic algorithms Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
linear model An analytical model that assumes linear relationships in the coefficients of the variables being studied.
linear regression A statistical technique used to find the best-fitting linear relationship between a target (dependent) variable and its predictors (independent variables).
logistic regression A linear regression that predicts the proportions of a categorical target variable, such as type of customer, in a population.
multidimensional database A database designed for on-line analytical processing. Structured as a multidimensional hypercube with one axis per dimension.
multiprocessor computer A computer that includes multiple processors connected by a network. See parallel processing.
nearest neighbor A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called a k-nearest neighbor technique.
non-linear model An analytical model that does not assume linear relationships in the coefficients of the variables being studied.
OLAP On-line analytical processing. Refers to array-oriented database applications that allow users to view, navigate through, manipulate, and analyze multidimensional databases.
outlier A data item whose value falls outside the bounds enclosing most of the other corresponding values in the sample. May indicate anomalous data. Should be examined carefully; may carry important information.
parallel processing The coordinated use of multiple processors to perform computational tasks. Parallel processing can occur on a multiprocessor computer or on a network of workstations or PCs.
predictive model A structure and process for predicting the values of specified variables in a dataset.
prospective data analysis Data analysis that predicts future trends, behaviors, or events based on historical data.
RAID Redundant Array of Inexpensive Disks. A technology for the efficient parallel storage of data for high-performance computer systems.
retrospective data analysis Data analysis that provides insights into trends, behaviors, or events that have already occurred.
rule induction The extraction of useful if-then rules from data based on statistical significance.
SMP Symmetric multiprocessor. A type of multiprocessor computer in which memory is shared among the processors.
terabyte One trillion bytes.
time series analysis The analysis of a sequence of measurements made at specified time intervals. Time is usually the dominating dimension of the data.

Thumbs upwell done I appreciate your interest, IF you want to do project on DATA MINING, just click here

High fiveMY this article is in two parts:- (so go to the NEXTPointing upafter this)

Data Mining: Storm cloudWhat is Data Mining?


Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a num66239909-databer of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Continuous Innovation

Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.


For example, one Midwest grocery chain used the data mining capacity of Oraclesoftware to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.

Data, Information, and Knowledge


Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes:

Español: Aspectos generales del Data Mining
Image via Wikipedia


  • operational or transactional data such as, sales, cost, inventory, payroll, and accounting
  • nonoperational data, such as industry sales, forecast data, and macro economic data
  • meta data – data about the data itself, such as logical database design or data dictionary definitions


The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when.


Information can be converted into knowledgeabout historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.

Data Warehouses

Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And, equally dramatic advances in data analysis software are allowing users to access this data freely. The data analysis software is what supports data mining.

What can data mining do?

Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data.

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.

WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradatadata warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.

The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scoutsoftware analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.

By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick’s defense and then finds Williams for an open jump shot.

How does data mining work?

While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:

  • Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
  • Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
  • Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
  • Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer’s purchase of sleeping bags and hiking shoes.DM_map_predict_1

Data mining consists of five major elements:

  • Extract, transform, and load transaction data onto the data warehouse system.
  • Store and manage the data in a multidimensional database system.
  • Provide data access to business analysts and information technology professionals.
  • Analyze the data by application software.
  • Present the data in a useful format, such as a graph or table.

Different levels of analysis are available:

  • Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
  • Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
  • Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID.
  • Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.
  • Rule induction: The extraction of useful if-then rules from data based on statistical significance.
  • Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.

What technological infrastructure is required?

Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for the smallest applications up to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCRhas the capacity to deliver applications exceeding 100 terabytes. There are two critical technological drivers:

  • Size of the database: the more data being processed and maintained, the more powerful the system required.
  • Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required.

Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.

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