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Author:

Mao, Guo-Jun (Mao, Guo-Jun.) | Sun, Xiao-Xi (Sun, Xiao-Xi.) | Zong, Dong-Jun (Zong, Dong-Jun.)

Indexed by:

EI Scopus PKU CSCD

Abstract:

In order to get valuable information, mining frequent itemsets from multidimensional data stream is needed. Through introduction of the concept of multidimensional item and multidimensional itemsets, the multidimensional data stream is expressed. A compact, compressed data structure MaxFP-Tree is designed to maintain multidimensional sets. Based on MaxFP-Tree, an incremental update algorithm to mine maximal frequent multidimensional itemsets is given. Experiment results show that the model and the algorithm of mining multidimensional data streams are efficient.

Keyword:

Data mining Trees (mathematics) Forestry

Author Community:

  • [ 1 ] [Mao, Guo-Jun]College of Computer Science, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Sun, Xiao-Xi]College of Computer Science, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Zong, Dong-Jun]College of Computer Science, Beijing University of Technology, Beijing 100124, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2010

Issue: 6

Volume: 36

Page: 820-827

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

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