Efficient partial multiple periodic patterns mining without redundant rules

Partial periodic patterns mining is a very interesting domain in data mining problem. In the previous studies, full and partial multiple periodic patterns mining problems are considered. The proposed methods, however, may produce redundant information and are inefficient. In this paper, a novel conc...

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Bibliographic Details
Published inProceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004 pp. 430 - 435 vol.1
Main Authors Wenpo Yang, Guanling Lee
Format Conference Proceeding
LanguageEnglish
Published Los Alamitos CA IEEE 2004
IEEE Computer Society Press
Subjects
Application software
Applied sciences
Computer applications
Computer science
Computer science; control theory; systems
Dairy products
Data engineering
Data mining
Data processing. List processing. Character string processing
Exact sciences and technology
Memory organisation. Data processing
Pattern analysis
Software
Software engineering
Time series analysis
Data analysis
Software development
Redundancy
Periodicity
Database
Information extraction
Data mining
Incomplete information
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Summary:Partial periodic patterns mining is a very interesting domain in data mining problem. In the previous studies, full and partial multiple periodic patterns mining problems are considered. The proposed methods, however, may produce redundant information and are inefficient. In this paper, a novel concept and new parameters are proposed to improve the performance of partial multiple periodic patterns mining. Instead of considering the whole database, the information needed for mining partial periodic patterns is transformed into a bit vector which can be stored in a main memory. A set of simulations is also performed to show the benefit of our approach.
ISBN:9780769522098
0769522092
ISSN:0730-3157
DOI:10.1109/CMPSAC.2004.1342875