Efficient discovery of interesting patterns using parallel algorithms is an actively studied topic in data mining. A key research issue related to this topic is data segmentation, which influences the overall computat...
详细信息
ISBN:
(数字)9783319985398
ISBN:
(纸本)9783319985398;9783319985381
Efficient discovery of interesting patterns using parallel algorithms is an actively studied topic in data mining. A key research issue related to this topic is data segmentation, which influences the overall computational requirements of an algorithm. This paper makes an effort to address this issue in correlated pattern mining. Two novel data segmentation techniques, 'database segmentation' and 'transaction segmentation,' have been introduced to discover the patterns efficiently. The former technique involves segmenting the database into multiple sub-databases such that each sub-database can be mined independently. The latter technique involves segmenting a transaction into multiple sub-transactions such that each sub-transaction can be processed as an individual transaction. The proposed techniques are algorithm independent, and therefore, can be incorporated into any parallel algorithm to find correlatedpatterns effectively. In this paper, we introduce map-reduce based pattern-growth algorithm by incorporating the above mentioned techniques. Experimental results demonstrate that the proposed algorithm is memory and runtime efficient and highly scalable as well.
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