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

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of oversampling the minority (abnormal)cla ss and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space)tha n only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space)t han varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC)and the ROC convex hull strategy.

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SMOTE: synthetic minority over-sampling technique

The Ribosomal Database Project (RDP) Classifier, a naive Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (≥95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.

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Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

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

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

/pdf/smote-synthetic-minority-over-sampling-technique-3s7jks70tk.pdf

SMOTE: Synthetic Minority Over-sampling Technique

By integrating syntactic and semantic processing, our parser (LAZY) is able to deterministically parse sentences which syntactically appear to be garden path sentences although native speakers do not need conscious reanalysis to understand them. LAZY comprises an extension to conceptual analysis which yields an explicit representation of syntactic information and a flexible interaction between semantic and syntactic knowledge.

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Conceptual Analysis of Garden-Path Sentences

Memory organization and explanation based learning

We investigate methods for tuning numeric parameters in Nynex MAX, a telephone trouble screening expert system. Steepest descent, hillclimbing, and simulated annealing parameter adjustment strategies are applied to the problems of maximizing classification accuracy and minimizing misclassification cost. For both of those optimization problems we evaluate each algorithm's ability to tune initial parameters for several situations. >

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Parameter tuning for the MAX expert system

We describe an artificial intelligence (AI) system (CTSHIV) that connects the scientific AIDS literature describing specific HIV drug resistances directly to the Customized Treatment Strategy of a specific HIV patient. Rules in the CTSHIV knowledge base encode knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance in the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment in order to infer current drug resistance. A search through mutation sequence space identifies nearby drug resistant mutant strains that might arise. The possible drug treatment regimens currently approved by the US Food and Drug Administration (FDA) are considered and ranked by their estimated ability to avoid identified current and nearby drug resistant mutants. The highest-ranked treatments are recommended to the attending physician. The result is more precise treatment of individual HIV patients, and a decreased tendency to select for drug resistant genes in the global HIV gene pool. The application is currently in use in human clinical trials on HIV patients. Initial results from a small clinical trial are encouraging and further clinical trials are planned. From an AI viewpoint the case study demonstrates the extensibility of knowledge-based systems because it illustrates how existing encoded knowledge can be used to support new applications that were unanticipated when the original knowledge was encoded.

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Knowledge-based avoidance of drug-resistant HIV mutants

Inductive Logic Programming (ILP) is an important and active subfield of machine learning. Unlike most of machine learning, ILP is concerned with learning first-order (or relational) rules, a representation that is more expressive than the attribute-value representation typically used by decision trees and neural networks. Due to the recent emergence of this field, most of the important work is scattered across a number of conference papers, journal papers, and theses. Most papers use slightly different notation, terminology, and definitions, which results in unnecessary confusion to newcomers to this research area. Inductive Logic Programming." Techniques and Applications is the first text that attempts to present an overview of the field, and it performs admirably at this task. While there are other books on ILP, some are edited volumes that do not provide extensive introductory material (e.g., Muggleton, 1992) and others (e.g., De Raedt, 1992; Morik, Wrobel, Kietz, & Emde, 1993) provide detailed reports on a single research project. Inductive Logic Programming: Techniques and Applications is appropriate as an introductory graduate text. It contains sufficient background material to gently introduce someone to the field, and it provides detailed descriptions of recent research contributions to the field. Since many ILP systems have sound theoretical foundations, the book contains a number of definitions and introduces some new notation. The definitions are illustrated with numerous examples that help to make the concepts concrete. The book does not suffer from a flaw I have seen in some theoretical treatments. There is no formalism for its own sake in this book. The definitions and notations introduced are used later in the book and help to clarify important concepts.

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Michael J. Pazzani

Papers

Conceptual Analysis of Garden-Path Sentences

Memory organization and explanation based learning

Parameter tuning for the MAX expert system

Knowledge-based avoidance of drug-resistant HIV mutants

Review of “Inductive Logic Programming: Techniques and Applications” by Nada Lavrač, Sašo Džeroski