The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.

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Instance-Based Learning Algorithms

The World Wide Web as a Global Information System has flooded us with a tremendous amount of data and information. Our capabilities of generating and collecting data have been increasing rapidly every day, in this age of Information Technology. This explosive growth in stored data has generated an urgent need for new technologies and automated tools to assist us in transforming the data into useful information and knowledge. Data Mining, popularly known as Knowledge Discovery in databases is the automated or convenient extraction of patterns representing knowledge implicitly stored in large databases which solves the above problem. This article explains What is data mining? and Why is it important? Also it deals about the basic concepts of data mining, data cluster and data mining rules. How to choose a data mining system with some examples also have been discussed in this article.

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

ness of Indexes Although cases are specific, indexes to cases need to be chosen so that the case can be used in as broad a selection of situations as appropriate. Often, this approach means indexes should be more abstract than the details of a particular case. Consider, for example, a case from CHEF (Hammond 1986, 1989). CHEF just created a recipe for beef and broccoli, a stir-fried dish. When it first created the recipe and tried it out, it found that the broccoli got soggy. It fixed the order of the steps in the recipe so that the broccoli remained crisp. This case could be indexed in several ways: (1) dish is prepared by stir frying, dish includes beef, and dish includes broccoli and (2) dish is prepared by stir frying, dish includes meat, and dish includes a crisp vegetable. The first set allows this case to be recalled whenever beef and broccoli are to be stir fried together. This index, however, would not allow recall of this case, for example, when chicken and snow peas are to be stir fried. However, the order of the steps probably has to be the same as for beef and broccoli—snow peas are also a crisp vegetable that should remain crisp. Indexing by the second set of descriptors makes this case more generally applicable. Concreteness of Indexes The danger of abstract indexes is that they can be so abstract that the reasoner would never realize that a new situation had these descriptors except through extensive inference. Thus, although indexes need to be generally applicable, they need to be concrete enough so that they can be recognized with little inference. Consider another example from CHEF to illustrate this point. CHEF just created a new recipe for a strawberry souffle. It created this dish by adapting a recipe for vanilla souffle. When it first made the souffle, it fell. CHEF figured out that the problem was that the liquids and leavening were not balanced: There was too much liquid for the amount of leavening in the recipe. It also figured out that the extra liquid was because of the juice in the strawberries. It solved the problem by increasing the leavening to counter the effect of the liquid in the strawberries. This case could be indexed in several ways: (1) dish is of type souffle, and liquids and leavening are not balanced; (2) dish is of type souffle, and dish includes strawberries; (3) dish is of type souffle, and dish includes fruit; and (4) dish is of type souffle, and dish has a lot of liquid. The last three indexes are clearly better Articles

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Improving human decision making through case-based decision aiding

A medical reasoning program that improves with experience.

A physician's problem-solving performance improves with experience. The performance of most medical expert systems does not. I have developed a diagnosis program for coronary disease that improves its performance by remembering and learning from cases that it has already solved. The program diagnoses commonly-seen problems efficiently by recalling similar, previous cases and adapting their solutions through simple modifications. When it lacks experience in solving a particular type of problem, the program resorts to reasoning from a physiological model, then remembers the solution for future use. The program can produce solutions identical to those derived by a model-based expert system for the same domain, but with an increase of two orders of magnitude in efficiency. The method described is independent of the particular domain and should be generally applicable.

A Medical Reasoning Program That Improves with Experience

Causal models can provide richly-detailed knowledge bases for producing explanations about behaviors in many domains, a task often termed interpretation or diagnosis. However, producing a causal explanation from the model can be time-consuming. This paper describes a system that solves a new problem by recalling a previous, similar problem and modifying its solution to fit the current problem. Because it is unlikely that any new problem will exactly match a previous one, the system evaluates differences between the problems using a set of evidence principles that allow the system to reason about such concepts as alternate lines of evidence, additional supporting evidence, and inconsistent evidence. If all differences between the new situation and the remembered situation are found to be insignificant, the previous causal explanation is adapted to fit the new case. This technique results in the same solution, but with an average of two orders of magnitude less effort. The evidence principles are domain independent, and the information necessary to apply them to other domain models is described.

Reasoning about evidence in causal explanations

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A medical reasoning program that improves with experience.

A Medical Reasoning Program That Improves with Experience

Reasoning about evidence in causal explanations