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Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 99 - Number 6 |
| Year of Publication: 2014 |
| Authors: Meenu Dave, Hitesh Maharwal |
10.5120/17377-7913
|
Meenu Dave, Hitesh Maharwal . Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset. International Journal of Computer Applications. 99, 6 ( August 2014), 20-23. DOI=10.5120/17377-7913
Abstract
Association rule mining plays a major role in decision making in the production and sales business area. It uses minimum support (minsup) and support confidence (supconf) as a base to generate the frequent patterns and strong association rules. Setting a single value of minsup for a transaction set doesn't seem feasible for some real life applications. Similarly the probabilistic value of items in the transaction set may be acceptable. So generating the frequent pattern from the uncertain dataset becomes a concern factor. This research work details the aforesaid problem and proposes a solution for the same.
References
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