• N.Asha Pranuthi
• Dr. Kondapalli Venkata Ramana

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits or revenues. Although there are many algorithms to extract utility itemsets, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. Earlier work shows this on two phase candidate generation. This approach suffers from scalability issue due to the huge number of candidates. Our paper presents the efficient approach where we can generate high utility patterns in one phase without generating candidates. Here we have take experiments on linear data structure, our pattern growth approach is to search a reverse set enumeration tree and to prune search space by utility upper bounding. Also high utility patterns are identified by a closure property and singleton property. This sort of large wide variety of candidate object units degrades the mining overall performance in phrases of execution time and area requirement. The situation may additionally end up worse at the same time as the database consists of prolonged transactions or long immoderate software object units. To overcome and speed up process we implemented pruning techniques. This paper proposes a novel algorithm that finds high utility patterns in a single phase without generating candidates. It uses a high utility pattern growth approach, a lookahead strategy, and a linear data structure. The pattern growth approach is to search a reverse set enumeration tree and to prune search space by utility upper bounding. We thus propose an efficient utility mining algorithm, which is a pruning approach (also termed the gradual pruning approach, or GPA), to discover high utility itemsets from a database. The Experimental consequences show that the proposed set of rules, especially application sample increase plus, required a lot less execution time and decreased reminiscence usage even as databases encompass lots of the excessive transactions.

Data mining, utility mining, high utility patterns, frequent patterns, pattern mining, gradual pruning approach

"Enhancing High Utility Pattern Mining using Gradual Pruning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.89-97, December-2018, Available :http://www.jetir.org/papers/JETIR1812914.pdf

"Enhancing High Utility Pattern Mining using Gradual Pruning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp89-97, December-2018, Available at : http://www.jetir.org/papers/JETIR1812914.pdf