Research Article
Mining of Rare Itemsets in Distributed Environment
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 105 - Issue 6 |
| Published: November 2014 |
| Authors: R N Yadawad, Rbv Subramanyam, U P Kulkarni |
10.5120/18378-9613
|
R N Yadawad, Rbv Subramanyam, U P Kulkarni . Mining of Rare Itemsets in Distributed Environment. International Journal of Computer Applications. 105, 6 (November 2014), 1-4. DOI=10.5120/18378-9613
@article{ 10.5120/18378-9613,
author = { R N Yadawad,Rbv Subramanyam,U P Kulkarni },
title = { Mining of Rare Itemsets in Distributed Environment },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 105 },
number = { 6 },
pages = { 1-4 },
doi = { 10.5120/18378-9613 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A R N Yadawad
%A Rbv Subramanyam
%A U P Kulkarni
%T Mining of Rare Itemsets in Distributed Environment%T
%J International Journal of Computer Applications
%V 105
%N 6
%P 1-4
%R 10.5120/18378-9613
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The mining of rare itemsets involves finding rarely occurring items. It is difficult to mine rare itemsets with a single minimum support (minsup) constraint because low minsup can result in generating too many rules in which some of them can be uninteresting [3]. In the literature [4, 5], "multiple minsup framework" was proposed to efficiently discover rare itemsets. However, that model still extracts uninteresting rules if the items' frequencies in a dataset vary widely. In this paper, we are using the notion of "item-to-pattern difference" and multiple minsup based FP-growth-like approach proposed in [6] to efficiently discover rare itemsets in the distributed environment. To discover global rare itemsets in distributed environment, information regarding itemsets of local sites is collected in the form of MIS-tree at one site; that is, each site sends its local MIS-tree to a single site where a global MIS-tree will be constructed from all the MIS-trees received from all the sites. This global MIS-tree is mined to generate global rare itemsets. Experimental results show that this approach is efficient in terms of communication bandwidth consumed.
References
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