High Performance Data Mining: Scaling Algorithms, Applications and Systems

by Yike Guo (Editor), R.L. Grossman (Editor)

Overview

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area.
High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

Product Details

ISBN-13:	9781475784152
Publisher:	Springer US
Publication date:	04/26/2013
Edition description:	2002
Pages:	106
Product dimensions:	5.98(w) x 9.02(h) x 0.01(d)

Table of Contents

Editorial; Yike Guo, R. Grossman. Parallel Formulations of Decision-Tree Classification Algorithms; A. Srivastava, Eui-Hong Han, V. Kumar, V. Singh. A Fast Parallel Clustering Algorithm for Large Spatial Databases; Xiaowei Xu, J. Jager, H.-P. Kiregel. Effect of Data Distribution in Parallel Mining of Associations; D.W. Cheung, Yongqiao Xiao. Parallel Learning of Belief Networks in Large and Difficult Domains; Y. Xiang, T. Chu.

Editorial Reviews

Contains four refereed papers covering important classes of data mining algorithms: classification, clustering, association rule discovery, and learning Bayesian networks. Srivastava et al present a detailed analysis of the parallelization strategy of tree induction algorithms. Xu et al present a parallel clustering algorithm for distributed memory machines. A new scalable algorithm for association rule discovery and a survey of other strategies is covered by Cheung et al. The final paper, written by Xiang et al, describes an algorithm for parallel learning of Bayesian networks. The papers aim to take a practical approach to large scale mining applications and increase useable knowledge concerning high performance computing technology. Lacks a subject index. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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