Performance based Frequent Itemset Mining Techniques for Data Mining
Sanjaydeep Singh Lodhi1, Ghanshyam Rathore2, Premnarayan Arya3
1Sanjaydeep Singh Lodhi, Department of Computer Application (Software Systems), S.A.T.I (Govt. Autonomous collage) , Vidisha, (M.P), India.
2Ghanshyam Rathore, Department of C.S.E. IIST, Indore (M.P), India.
3Premnarayan Arya, Asst. Prof. Dept. of CA (Software Systems), S.A.T.I (Govt. Autonomous collage) , Vidisha, (M.P), India.
Manuscript received on may 22, 2012. | Revised Manuscript received on June 12, 2012. | Manuscript published on June 30, 2012. | PP: 114-121 | Volume-1 Issue-5, June 2012 | Retrieval Number:  E0439051512/2012©BEIESP

Open Access | Ethics and  Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. There is a large body of research on Frequent Itemset Mining (FIM) but very little work addresses FIM in uncertain databases. Most studies on frequent itemset mining focus on mining precise data. However, there are situations in which the data are uncertain. This leads to the mining of uncertain data. There are also situations in which users are only interested in frequent itemsets that satisfy user-specified aggregate constraints. This leads to constrained mining of uncertain data. Moreover, floods of uncertain data can be produced in many other situations. This leads to stream mining of uncertain data. In this paper, we propose algorithms to deal with all these situations. We first design a tree-based mining algorithm to find all frequent itemsets from databases of uncertain data. We then extend it to mine databases of uncertain data for only those frequent itemsets that satisfy user-specified aggregate constraints and to mine streams of uncertain data for all frequent itemsets. Our experimental results show the more effectiveness than existing methods. 
Keywords: Data Mining, Frequent Itemset Mining, Apriori Algorithm.