International Journal of Computational Intelligence Systems

Volume 6, Issue 2, March 2013, Pages 209 - 222

Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database

Authors
Bhawna Mallick, Deepak Garg, P. S. Grover
Corresponding Author
Bhawna Mallick
Received 27 January 2012, Accepted 6 September 2012, Available Online 1 March 2013.
DOI
https://doi.org/10.1080/18756891.2013.768432How to use a DOI?
Keywords
Sequential pattern mining, CFM-PrefixSpan, Progressive database, updated CFM-tree, progressive CFM patterns, algorithms
Abstract

Sequential pattern mining is a vital data mining task to discover the frequently occurring patterns in sequence databases. As databases develop, the problem of maintaining sequential patterns over an extensively long period of time turn into essential, since a large number of new records may be added to a database. To reflect the current state of the database where previous sequential patterns would become irrelevant and new sequential patterns might appear, there is a need for efficient algorithms to update, maintain and manage the information discovered. Several efficient algorithms for maintaining sequential patterns have been developed. Here, we have presented an efficient algorithm to handle the maintenance problem of CFM-sequential patterns (Compact, Frequent, Monetary-constraints based sequential patterns). In order to efficiently capture the dynamic nature of data addition and deletion into the mining problem, initially, we construct the updated CFM-tree using the CFM patterns obtained from the static database. Then, the database gets updated from the distributed sources that have data which may be static, inserted, or deleted. Whenever the database is updated from the multiple sources, CFM tree is also updated by including the updated sequence. Then, the updated CFM-tree is used to mine the progressive CFM-patterns using the proposed tree pattern mining algorithm. Finally, the experimentation is carried out using the synthetic and real life distributed databases that are given to the progressive CFM-miner. The experimental results and analysis provides better results in terms of the generated number of sequential patterns, execution time and the memory usage over the existing IncSpan algorithm.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 2
Pages
209 - 222
Publication Date
2013/03/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.768432How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Bhawna Mallick
AU  - Deepak Garg
AU  - P. S. Grover
PY  - 2013
DA  - 2013/03/01
TI  - Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database
JO  - International Journal of Computational Intelligence Systems
SP  - 209
EP  - 222
VL  - 6
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.768432
DO  - https://doi.org/10.1080/18756891.2013.768432
ID  - Mallick2013
ER  -