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

We review accuracy estimation methods and compare the two most common methods crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical re cults in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment--over half a million runs of C4.5 and a Naive-Bayes algorithm--to estimate the effects of different parameters on these algrithms on real-world datasets. For crossvalidation we vary the number of folds and whether the folds are stratified or not, for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, The best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.

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A study of cross-validation and bootstrap for accuracy estimation and model selection

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

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Machine learning in automated text categorization

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

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Statistical pattern recognition: a review

We describe the results of extensive experiments using optimized rule-based induction methods on large document collections. The goal of these methods is to discover automatically classification patterns that can be used for general document categorization or personalized filtering of free text. Previous reports indicate that human-engineered rule-based systems, requiring many man-years of developmental efforts, have been successfully built to “read” documents and assign topics to them. We show that machine-generated decision rules appear comparable to human performance, while using the identical rule-based representation. In comparison with other machine-learning techniques, results on a key benchmark from the Reuters collection show a large gain in performance, from a previously reported 67% recall/precision breakeven point to 80.5%. In the context of a very high-dimensional feature space, several methodological alternatives are examined, including universal versus local dictionaries, and binary versus frequency-related features.

Automated learning of decision rules for text categorization

Preface 1 Overview of Learning Systems 1.1 What is a Learning System? 1.2 Motivation for Building Learning Systems 1.3 Types of Practical Empirical Learning Systems 1.3.1 Common Theme: The Classification Model 1.3.2 Let the Data Speak 1.4 What's New in Learning Methods 1.4.1 The Impact of New Technology 1.5 Outline of the Book 1.6 Bibliographical and Historical Remarks 2 How to Estimate the True Performance of a Learning System 2.1 The Importance of Unbiased Error Rate Estimation 2.2. What is an Error? 2.2.1 Costs and Risks 2.3 Apparent Error Rate Estimates 2.4 Too Good to Be True: Overspecialization 2.5 True Error Rate Estimation 2.5.1 The Idealized Model for Unlimited Samples 2.5.2 Train-and Test Error Rate Estimation 2.5.3 Resampling Techniques 2.5.4 Finding the Right Complexity Fit 2.6 Getting the Most Out of the Data 2.7 Classifier Complexity and Feature Dimensionality 2.7.1 Expected Patterns of Classifier Behavior 2.8 What Can Go Wrong? 2.8.1 Poor Features, Data Errors, and Mislabeled Classes 2.8.2 Unrepresentative Samples 2.9 How Close to the Truth? 2.10 Common Mistakes in Performance Analysis 2.11 Bibliographical and Historical Remarks 3 Statistical Pattern Recognition 3.1 Introduction and Overview 3.2 A Few Sample Applications 3.3 Bayesian Classifiers 3.3.1 Direct Application of the Bayes Rule 3.4 Linear Discriminants 3.4.1 The Normality Assumption and Discriminant Functions 3.4.2 Logistic Regression 3.5 Nearest Neighbor Methods 3.6 Feature Selection 3.7 Error Rate Analysis 3.8 Bibliographical and Historical Remarks 4 Neural Nets 4.1 Introduction and Overview 4.2 Perceptrons 4.2.1 Least Mean Square Learning Systems 4.2.2 How Good Is a Linear Separation Network? 4.3 Multilayer Neural Networks 4.3.1 Back-Propagation 4.3.2 The Practical Application of Back-Propagation 4.4 Error Rate and Complexity Fit Estimation 4.5 Improving on Standard Back-Propagation 4.6 Bibliographical and Historical Remarks 5 Machine Learning: Easily Understood Decision Rules 5.1 Introduction and Overview 5.2 Decision Trees 5.2.1 Finding the Perfect Tree 5.2.2 The Incredible Shrinking Tree 5.2.3 Limitations of Tree Induction Methods 5.3 Rule Induction 5.3.1 Predictive Value Maximization 5.4 Bibliographical and Historical Remarks 6 Which Technique is Best? 6.1 What's Important in Choosing a Classifier? 6.1.1 Prediction Accuracy 6.1.2 Speed of Learning and Classification 6.1.3 Explanation and Insight 6.2 So, How Do I Choose a Learning System? 6.3 Variations on the Standard Problem 6.3.1 Missing Data 6.3.2 Incremental Learning 6.4 Future Prospects for Improved Learning Methods 6.5 Bibliographical and Historical Remarks 7 Expert Systems 7.1 Introduction and Overview 7.1.1 Why Build Expert Systems? New vs. Old Knowledge 7.2 Estimating Error Rates for Expert Systems 7.3 Complexity of Knowledge Bases 7.3.1 How Many Rules Are Too Many? 7.4 Knowledge Base Example 7.5 Empirical Analysis of Knowledge Bases 7.6 Future: Combined Learning and Expert Systems 7.7 Bibliographical and Historical Remarks References Author Index Subject Index

Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business. This information is overwhelmingly text and has its record-keeping purpose, but an automated analysis might be desirable to find patterns in the stored records. Analogous to this data mining is text mining, which also finds patterns and trends in information samples but which does so with far less structured--though with greater immediate utility for users--ingredients. This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. It introduces the new world of text mining and examines proven methods for various critical text-mining tasks, such as automated document indexing and information retrieval and search. New research areas are explored, such as information extraction and document summarization, that rely on evolving text-mining techniques.

Text Mining: Predictive Methods for Analyzing Unstructured Information

1 What is Data Mining? 2 Statistical Evaluation for Big Data 3 Preparing the Data 4 Data Reduction 5 Looking for Solutions 6 What's Best for Data Reduction and Mining? 7 Art or Science? Case Studies in Data Mining

Predictive Data Mining: A Practical Guide

https://people.dbmi.columbia.edu/~ehs7001/Clancey-Shortliffe-1984/Ch7.pdf

Sholom M. Weiss

Papers

Automated learning of decision rules for text categorization

Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems

Text Mining: Predictive Methods for Analyzing Unstructured Information

Predictive Data Mining: A Practical Guide

A model-based method for computer-aided medical decision-making