Data Mining Techniques

This book provides a powerful tool for extracting answers to these and other important business questions from the corporate databases in which they are buried. Data mining has grown into an essential tool for modern business. In this latest version, we have added a few new chapters with expensive updates and revisions to all chapters.

This book maintains the focus of the previous version by showing marketing analysts, business managers, and data mining professionals how to use data mining methods and techniques to solve a variety of problems. While never sacrificing accuracy for simplification, even complex topics in clear form minimize the use of terms or formulas and use concise English. Technical topics are illustrated with case studies and real-world examples drawn from my experience, and all chapters contain useful tips for practitioners. New covered or detailed covered methods include linear and logistic regression, and clustering methods. After using an overview of
Data Mining Applications are used to establish a business context and introduce aspects of data mining methodologies common to all data mining projects; this book provides all important data mining techniques.

Data mining is a key step in the KDD process, extracting interesting patterns from a set of data sources. The obtained patterns are used for conceptual explanation, related analysis, construction of classification and regression models, data clustering, data modeling, time-series trend modeling, and detection of outliers. Because patterns present in the data are not equally useful, measures of interest are needed to estimate the relevance of detected patterns to guide the data mining process.

This book emphasizes the importance of measures of interest and demonstrates standard simplicity, but does not confer, although detailed, data processing of alternative measures of interest, measuring certainty, usefulness, and novelty of interest to minors.

Data warehousing and multidimensional databases were originally introduced as desirable interlayers between data sources, allowing users to interact with OLAM to integrate online analytical processing and data mining.

In the first chapter of this book, the reader will find an excellent overview of data warehousing concepts and a proposal for an integrated OLAM architecture.