Retrieving Frequent Item Sets from Distributed Data Base
Hamideh Hajiabadi1, Saeideh Kabiri Rad2
1Hamideh Hajiabadi, Birjand University of Technology, Iran.
2Saeideh Kabiri Rad, Birjand University of Technology, Iran.

Manuscript received on November 02, 2014. | Revised Manuscript received on November 04, 2014. | Manuscript published on November 05, 2014. | PP: 64-66 | Volume-4 Issue-5, November 2014. | Retrieval Number: D2357094414 /2014©BEIESP
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Abstract: With fast growing of the network and the data storage, large scale data are rapidly expanded and collected on the physically distributed storage, consequently traditional data mining approaches are not appropriate for information retrieval purpose. Distributed data mining techniques are developed in order to examine distributed data by parallel algorithms. Distributed data mining algorithms based on finding frequent itemsets are widely used for this purpose. The itemsets retrieved are numerous. In this paper proposed a tree based mining approach contributing to user such that reducing number of retrieved itremsets. The algorithm is implemented and the results are demonstrated.
Keywords: Frequent itemsets, Non-Derivable itemsets, Distributed database.