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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

https://ai.stanford.edu/~ronnyk/wrappersPrint.pdf

Wrappers for feature subset selection

Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

Rough sets

A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. Further experiments compared CFS with a wrapper—a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets. Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates iii feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.

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Correlation-based Feature Selection for Machine Learning

The paper explores the concepts of approximate relations and functions in the framework of the theory of rough sets. The difficulties with the application of the idea of rough relation to general rough function definition are discussed. The definition of rough function for the domain of real numbers is introduced and its properties are investigated in detail including the generalization of the standard notion of function continuity known in the theory of real functions.

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Rough sets, rough relations and rough functions

Rough set theory was proposed by Zdzislaw Pawlak (1982, 1991) in the early 1980s. Since then we have witnessed a systematic, worldwide growth of interest in rough set theory and its applications. The rough set approach has been introduced to deal with vague or imprecise concepts, to derive knowledge from data, and to reason about knowledge derived from data. In the first part of this chapter we outline the basic notions of rough sets, especially those that are related to knowledge extraction from data. Searching for knowledge is usually guided by some constraints (Langley et al., 1987). A wide class of such constraints can be expressed by discernibility of objects. Knowledge derived from data by the rough set approach consists of different constructs. Among them there are reducts, which are the central construct in the rough set approach, different kinds of rules (such as decision rules or association rules), dependencies, and patterns (templates), or classifiers. The reducts are of special importance since all other constructs can be derived from different kinds of reducts using the rough set approach. Strategies for searching reducts apply Boolean (propositional) reasoning (Brown, 1990), since the constraints (e.g., constraints related to the discernibility of objects) are expressible by propositional formulas. Moreover, using Boolean reasoning, minimal description-length data models (Mitchell, 1997; Rissanen, 1978) can be induced since they correspond to constructs of Boolean functions called prime implicants (or their approximations). The second part of this chapter includes illustrative examples of applications of this general scheme to inducing from data various forms of knowledge.

Rough sets perspective on data and knowledge

W: ICS PAS Reports 429/81, pages 1-20. Institute of Computer Science Polish Academy of Sciences (ICS PAS), Warsaw, Poland, 1981

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Classification of objects by means of attributes

The concept of “rough” sets is used to approximate the analysis of an information system describing 77 patients with duodenal ulcer treated by highly selective vagotomy (HSV). The patients are described by 11 attributes. The attributes concern sex, age, duration of disease, complication of duodenal ulcer and various factors of gastric secretion. Two values of sex and age are distinguished, five values of complications and three or four values of secretion attributes, according to norms proposed in this paper. For each patient, the result of treatment by HSV is expressed in the Visick grading which corresponds to four classes. Using the method of rough classification it is shown that the given norms ensure a good classification of patients. Afterwards, some minimum sets of attributes significant for high-quality classification are obtained. Upon analysis of values taken by attributes belonging to these sets a “model” of patients in each class is constructed. This model gives indications for treatment by HSV.

Rough classification of patients after highly selective vagotomy for duodenal ulcer

The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for computer scientists, particularly in the area of artificial intelligence. There are many approaches to the problem of how to understand and manipulate the imperfect knowledge. The most successful one is, no doubt, fuzzy set theory proposed by Zadeh. Rough set theory is another attempt to this problem. The theory has attracted attention of many researchers and practitioners all over the world, who contributed essentially to its development and applications. Rough set theory overlaps with many other theories, especially with fuzzy set theory, evidence theory and Boolean reasoning methods-nevertheless it can be viewed in its own rights, as an independent, complementary, and not competing discipline.

Zdzisław Pawlak

Papers

Rough sets, rough relations and rough functions

Rough sets perspective on data and knowledge

Classification of objects by means of attributes

Rough classification of patients after highly selective vagotomy for duodenal ulcer

Why rough sets