Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database
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 adde...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2013-04-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868380.pdf |
| _version_ | 1828399156999225344 |
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| author | Bhawna Mallick Deepak Garg P. S. Grover |
| author_facet | Bhawna Mallick Deepak Garg P. S. Grover |
| author_sort | Bhawna Mallick |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T09:13:19Z |
| format | Article |
| id | doaj.art-3e05d99e985c424493b5f9705583a678 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-10T09:13:19Z |
| publishDate | 2013-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-3e05d99e985c424493b5f9705583a6782022-12-22T01:54:56ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832013-04-016210.1080/18756891.2013.768432Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive DatabaseBhawna MallickDeepak GargP. S. GroverSequential 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.https://www.atlantis-press.com/article/25868380.pdfSequential pattern miningCFM-PrefixSpanProgressive databaseupdated CFM-treeprogressive CFM patternsalgorithms |
| spellingShingle | Bhawna Mallick Deepak Garg P. S. Grover Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database International Journal of Computational Intelligence Systems Sequential pattern mining CFM-PrefixSpan Progressive database updated CFM-tree progressive CFM patterns algorithms |
| title | Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database |
| title_full | Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database |
| title_fullStr | Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database |
| title_full_unstemmed | Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database |
| title_short | Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database |
| title_sort | progressive cfm miner an algorithm to mine cfm sequential patterns from a progressive database |
| topic | Sequential pattern mining CFM-PrefixSpan Progressive database updated CFM-tree progressive CFM patterns algorithms |
| url | https://www.atlantis-press.com/article/25868380.pdf |
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