| Clustering and Information Retrieval 2004 Edition Contributor(s): Weili Wu (Editor), Hui Xiong (Editor), Shekhar, S. (Editor) |
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ISBN: 1402076827 ISBN-13: 9781402076824 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: November 2003 Annotation: This volume contains recent developments in clustering and information retrieval, including clustering algorithms, evaluation methodologies, and architectures for information retrieval. It provides a survey of the state-of-the-art research in clustering and information retrieval. Audience: This volume is suitable for professionals and researchers in data mining and information retrieval. It is also appropriate for use in graduate courses. |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Computers | System Administration - Storage & Retrieval - Computers | Information Theory |
| Dewey: 005.7 |
| LCCN: 2003062061 |
| Series: Network Theory and Applications |
| Physical Information: 1.01" H x 7.1" W x 8.98" (1.55 lbs) 330 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Clustering is an important technique for discovering relatively dense sub-regions or sub-spaces of a multi-dimension data distribution. Clus- tering has been used in information retrieval for many different purposes, such as query expansion, document grouping, document indexing, and visualization of search results. In this book, we address issues of cluster- ing algorithms, evaluation methodologies, applications, and architectures for information retrieval. The first two chapters discuss clustering algorithms. The chapter from Baeza-Yates et al. describes a clustering method for a general metric space which is a common model of data relevant to information retrieval. The chapter by Guha, Rastogi, and Shim presents a survey as well as detailed discussion of two clustering algorithms: CURE and ROCK for numeric data and categorical data respectively. Evaluation methodologies are addressed in the next two chapters. Ertoz et al. demonstrate the use of text retrieval benchmarks, such as TRECS, to evaluate clustering algorithms. He et al. provide objective measures of clustering quality in their chapter. Applications of clustering methods to information retrieval is ad- dressed in the next four chapters. Chu et al. and Noel et al. explore feature selection using word stems, phrases, and link associations for document clustering and indexing. Wen et al. and Sung et al. discuss applications of clustering to user queries and data cleansing. Finally, we consider the problem of designing architectures for infor- mation retrieval. Crichton, Hughes, and Kelly elaborate on the devel- opment of a scientific data system architecture for information retrieval. |