Isbn: 9780471731900 - mining graph data (9 Ergebnisse)

Hardcover. Zustand: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. xix + 479 Illus.

Zustand: New. In English.

Gebunden. Zustand: New. DIANE J. COOK, PhD, is the Huie-Rogers Chair Professor in the School of Electrical Engineering and Computer Science at Washington State University. Her extensive research in artificial intelligence and data mining has been supported by grants from the Natio.

Hardcover. Zustand: gut. 2006. Mining Graph Data In englischer Sprache. pages.

Hardcover. Zustand: Brand New. 1st edition. 500 pages. 9.50x6.25x1.00 inches. In Stock.

Zustand: New. This books illustrates how data mining techniques, through the application of algorithms and graphs, have been responding to the need for the collection and storage of larger and more complex volumes of data. Editor(s): Cook, Diane J.; Holder, Lawrence B. Num Pages: 500 pages, Illustrations. BIC Classification: PBK. Category: (P) Professional & Vocational. Dimension: 235 x 163 x 33. Weight in Grams: 842. . 2006. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Buch. Zustand: Neu. Neuware - Discover the latest data mining techniques for analyzing graph dataThis text takes a focused and comprehensive look at an area of data mining that is quickly rising to the forefront of the field: mining data that is represented as a graph. Each chapter is written by a leading researcher in the field; collectively, the chapters represent the latest findings and applications in both theory and practice, including solutions to many of the algorithmic challenges that arise in mining graph data. Following the authors' step-by-step guidance, even readers with minimal background in analyzing graph data will be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets.Mining Graph Data is divided into three parts:\* Part I, Graphs, offers an introduction to basic graph terminology and techniques.\* Part II, Mining Techniques, features a detailed examination of computational techniques for extracting patterns from graph data. These techniques are the state of the art in frequent substructure mining, link analysis, graph kernels, and graph grammars.\* Part III, Applications, describes the application of data mining techniques to four graph-based application domains: chemical graphs, bioinformatics data, Web graphs, and social networks.Practical case studies are included in many of the chapters. An accompanying Web site features source code and datasets, offering readers the opportunity to experiment with the techniques presented in the book as well as test their own ideas on graph data. The Web site also includes the results of many of the techniques presented in the text.This landmark work is intended for students and researchers in computer science, information systems, and data mining who want to learn how to analyze and extract useful patterns and concepts from graph data.…