A Novel Algorithm to Estimate High Utility Item Sets and to Achieve Privacy Based on Transaction Splitting

R.Chitra Devi; S.Venkatesh Babu

CALL FOR PAPERS : Dec-2023

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25-Dec-23

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A Novel Algorithm to Estimate High Utility Item Sets and to Achieve Privacy Based on Transaction Splitting

Author(s):

R.Chitra Devi , Christian College of Engineering and Technology,Oddanchatram, Tamilnadu-624619, India; S.Venkatesh Babu, Christian College of Engineering and Technology,Oddanchatram, Tamilnadu-624619, India

Keywords:

Frequent item set mining, Utility Mining, Data Mining, closest item set mining

Abstract:

Data mining methods are very useful in order to determine the hidden motivating and necessary information from the huge database. Extracting high utility item sets (HUI) and frequent item set mining are become a hectic task in data mining techniques. Frequent item set is a famous technique for identifying the items purchased together. But, frequent item set treats all the items in the same precedence. Even though it could identify the items, in some cases it fails to address the quantity of the rare product. HUI determines the item with high profit. Frequent item set mining is the major issue in data mining because it does not fulfill the requirement of users who desire to discover item sets with high utilities such as high profits. HUI determines the item with high profit. In many cases HUI will reduce the efficiency. The utility represents the importance of the product. In this paper, to overcome these issue we introduced the new concept of finding HUI with the closest item set. For the longer transactions new splitting technique was introduced rather than the truncation to maintain the privacy.