The Handbook of Data Mining

The Handbook of Data Mining

The Handbook of Data Mining

The Handbook of Data Mining

Synopsis

Advanced technologies have enabled the collection of large amounts of data in many fields. This data contains valuable information and knowledge that heretofore could not be used. Up until now, the ability to manually process this large amount of data was an unwieldy task. Automated tools were needed to "mine" the useful information from the large amounts of data. Now that such automated tools are available, data mining techniques are becoming more popular in many areas of research and development. Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world applications to ease understanding of the materials. Organized into three parts, Part I presents various data mining methodologies, concepts, and available software tools for each methodology. Part II addresses various issues typically faced in the management of data mining projects and tips on how to maximize outcome utility. Part III features numerous real-world applications of these techniques in a variety of areas, including human performance, geospatial, bioinformatics, on- and off-line customer transaction activity, security-related computer audits, network traffic, text and image, and manufacturing quality. Ideal for researchers and developers who want to use data mining techniques to derive scientific inferences where extensive data is available in scattered reports and publications. This handbook is also an excellent resource for graduate-level courses on data mining and decision and expert systems methodology.

Excerpt

Advanced technologies have enabled us to collect large amounts of data on a continuous or periodic basis in many fields. On one hand, these data present the potential for us to discover useful information and knowledge that we could not see before. On the other hand, we are limited in our ability to manually process large amounts of data to discover useful information and knowledge. This limitation demands automatic tools for data mining to mine useful information and knowledge from large amounts of data. Data mining has become an active area of research and development. This book presents comprehensive coverage of data mining concepts, algorithms, methodologies, management issues, and tools, which are all illustrated through simple examples and real-world applications for an easy understanding and mastering of those materials. Necessary materials for data mining are presented and organized coherently in one volume. This enables one to quickly start and conduct research work or practical applications of data mining without spending precious time searching for those materials from many different sources. Advanced topics in the area of data mining are also introduced in this handbook with extensive references for further readings.

Materials in this handbook are organized into three parts on methodologies, management, and applications of data mining, respectively. Part I includes chapters 1–13, which present various data mining methodologies, including concepts, algorithms, and available software tools for each methodology. Chapter 1 describes decision trees—a widely used data mining methodology in practice for learning prediction and classification models, data patterns, and so on. Chapter 2 introduces association rules—a data mining methodology that is usually used to discover frequently cooccurring data items, for example, items that are commonly purchased together by customers at grocery stores. Chapter 3 presents artificial neural networks with some models, which support the supervised learning of prediction and classification models, and other models, which support unsupervised learning of data structures. Chapter 4 describes statistical process control techniques that can be used to reveal data similarities and differences, especially when dealing with two categories of data: data produced in normal conditions and data produced in abnormal conditions, for anomaly detection. Chapter 5 introduces the Bayesian approach to various data mining problems. Chapter 6 presents hidden Markov models that can be used to mine sequential data patterns. Chapter 7 provides a coherent framework for a systematic understanding of existing prediction and classification models for data mining. Chapter 8 describes principal components analysis—a statistical method that is often used to reduce data dimensions and reveal the underlying structure of data. Chapter 9 introduces psychometric methods of latent variable modeling that can be used to reveal unobserved latent variables and their relationships with observed data variables. Chapter 10 presents both traditional clustering methods and advanced methods for scalable clustering of data to generate homogeneous groups of data and reveal data patterns. Chapter 11 presents methods of determining similarity of time-series data and generating the index of time-series data for data retrieval based on time-series similarity. Chapter 12 introduces nonlinear time-series analysis methods, including wavelet analysis, which may not be well known to the data mining . . .

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