GDL

Efficient Support Coupled Frequent Pattern Mining Over Progressive Databases

ISSN : 0975-5705
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Keshavamurthy B.N, Mitesh Sharma and Durga Toshniwal, International Journal of Database Management Systems
Dibuat : 2012-06-26, dengan 1 file

Keyword : Progressive sequential pattern, sequential pattern
Subjek : Efficient Support Coupled Frequent Pattern Mining Over Progressive Databases
Url : http://airccse.org/journal/ijdms/papers/0510ijdms05.pdf
Sumber pengambilan dokumen : Internet

There have been many recent studies on sequential pattern mining. The sequential pattern mining on


progressive databases is relatively very new, in which we progressively discover the sequential patterns in


period of interest. Period of interest is a sliding window continuously advancing as the time goes by. As


the focus of sliding window changes , the new items are added to the dataset of interest and obsolete


items are removed from it and become up to date. In general, the existing proposals do not fully explore


the real world scenario, such as items associated with support in data stream applications such as market


basket analysis. Thus mining important knowledge from supported frequent items becomes a non trivial


research issue. Our proposed novel approach efficiently mines frequent sequential pattern coupled with


support using progressive mining tree.

Deskripsi Alternatif :

There have been many recent studies on sequential pattern mining. The sequential pattern mining on


progressive databases is relatively very new, in which we progressively discover the sequential patterns in


period of interest. Period of interest is a sliding window continuously advancing as the time goes by. As


the focus of sliding window changes , the new items are added to the dataset of interest and obsolete


items are removed from it and become up to date. In general, the existing proposals do not fully explore


the real world scenario, such as items associated with support in data stream applications such as market


basket analysis. Thus mining important knowledge from supported frequent items becomes a non trivial


research issue. Our proposed novel approach efficiently mines frequent sequential pattern coupled with


support using progressive mining tree.

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