By Nong Ye
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.