𝔖 Bobbio Scriptorium
✦   LIBER   ✦

High Performance Data Mining

✍ Scribed by Guo, Grossman. (eds.)


Book ID
127424418
Publisher
Kluwer
Year
2000
Tongue
English
Weight
1 MB
Category
Library
ISBN-13
9780306470110

No coin nor oath required. For personal study only.

✦ Synopsis


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.

✦ Subjects


Интеллектуальный анализ данных


📜 SIMILAR VOLUMES


Special Issue on High-Performance Data M
✍ Vipin Kumar; Sanjay Ranka; Vineet Singh 📂 Article 📅 2001 🏛 Elsevier Science 🌐 English ⚖ 70 KB

There has been an explosive growth in the amount of data collected by commercial and scientific applications. E-commerce companies store click stream data to track consumer behavior on a Web site. Particle accelerators routinely store information regarding millions of events every day for high-energ

High Performance Multidimensional Analys
✍ Goil S., Choudhary A. 📂 Library 📅 1998 🌐 English ⚖ 295 KB

Summary information from data in large databases is used to answer queries in On-Line Analytical Processing (OLAP) systems and to build decision support systems over them. The Data Cube is used to calculate and store summary information on a variety of dimensions, which is computed only partially if