GDL

ETP-Mine: An Efficient Method for Mining Transitional Patterns

ISSN : 0975-5705
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : B. Kiran Kumar and A. Bhaskar, International Journal of Database Management Systems
Dibuat : 2012-06-26, dengan 1 file

Keyword : Frequent Patterns, Association Rules, Transitional Patterns, Significant milestones.
Subjek : ETP-Mine: An Efficient Method for Mining Transitional Patterns
Url : http://airccse.org/journal/ijdms/papers/0810ijdms03.pdf
Sumber pengambilan dokumen : Internet

A Transaction database contains a set of transactions along with items and their associated timestamps.


Transitional patterns are the patterns which specify the dynamic behavior of frequent patterns in a


transaction database. To discover transitional patterns and their significant milestones, first we have to


extract all frequent patterns and their supports using any frequent pattern generation algorithm. These


frequent patterns are used in the generation of transitional patterns. The existing algorithm (TP-Mine)


generates frequent patterns, some of which cannot be used in generation of transitional patterns. In this


paper, we propose a modification to the existing algorithm, which prunes the candidate items to be used in


the generation of frequent patterns. This method drastically reduces the number of frequent patterns which


are used in discovering transitional patterns. Extensive simulation test is done to evaluate the proposed


method.

Deskripsi Alternatif :

A Transaction database contains a set of transactions along with items and their associated timestamps.


Transitional patterns are the patterns which specify the dynamic behavior of frequent patterns in a


transaction database. To discover transitional patterns and their significant milestones, first we have to


extract all frequent patterns and their supports using any frequent pattern generation algorithm. These


frequent patterns are used in the generation of transitional patterns. The existing algorithm (TP-Mine)


generates frequent patterns, some of which cannot be used in generation of transitional patterns. In this


paper, we propose a modification to the existing algorithm, which prunes the candidate items to be used in


the generation of frequent patterns. This method drastically reduces the number of frequent patterns which


are used in discovering transitional patterns. Extensive simulation test is done to evaluate the proposed


method.

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