Improvements in the data partitioning approach for frequent itemsets mining

Nguyen, Son N. and Orlowska, Maria E. (2005). Improvements in the data partitioning approach for frequent itemsets mining. In: A. Jorge, L. Torgo, P. Brazdil, R. Camacho and J. Gama, Knowledge Discovery in Databases: PKDD 2005. 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-05), Porto, Portugal, (625-633). 3-7 October 2005. doi:10.1007/11564126_66


Author Nguyen, Son N.
Orlowska, Maria E.
Title of paper Improvements in the data partitioning approach for frequent itemsets mining
Conference name 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-05)
Conference location Porto, Portugal
Conference dates 3-7 October 2005
Proceedings title Knowledge Discovery in Databases: PKDD 2005   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2005
Sub-type Fully published paper
DOI 10.1007/11564126_66
ISBN 9783540292449
3540292446
ISSN 0302-9743
1611-3349
Editor A. Jorge
L. Torgo
P. Brazdil
R. Camacho
J. Gama
Volume 3721
Start page 625
End page 633
Total pages 9
Collection year 2005
Language eng
Abstract/Summary Frequent Itemsets mining is well explored for various data types, and its computational complexity is well understood. There are methods to deal effectively with computational problems. This paper shows another approach to further performance enhancements of frequent items sets computation. We have made a series of observations that led us to inventing data pre-processing methods such that the final step of the Partition algorithm, where a combination of all local candidate sets must be processed, is executed on substantially smaller input data. The paper shows results from several experiments that confirmed our general and formally presented observations.
Keyword Association rules
Frequent itemset
Partition
Performance
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Series title: Lecture Notes in Computer Science; Subseries title: Lecture Notes in Artificial Intelligence

 
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Created: Thu, 23 Aug 2007, 21:00:50 EST