Efficient Mining of High Utility Itemsets from Large Datasets

-Identifying frequent items from database and treating each item in a database as equal. However, items are actually differs in many aspects like, profit in real application, such as retail marketing. The difference between items makes a strong impact on the decision making applications, where the values of each items are considered as utilities. Utility mining focuses on identifying the itemsets with high utility like profit, aesthetic value. High utility itemsets mining extends frequent pattern mining to discover itemsets in a large database with utility values above a given threshold. Here we use two algorithms UP-Growth and FP-Growth for mining high utility itemsets and frequent users with a set of effective strategies. The information of high utility itemsets is maintained in a UPTree. Candidate itemsets are generated efficiently. Customer Relationship Management (CRM) is incorporated into the system by tracking the customers who are frequent buyers of the different kinds of item sets.

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“…So, the issue of high utility itemset mining is raised. And many studies [2], [4], [6], [7], [8] have addressed this problem.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Discovery of High Utility Item Setsfrom Large Database

Santhamani¹,

Premkumar²,

Gayathri³

et al. 2013

IJCATR

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“…Association rule mining is considered to be an interesting research area and studied widely [1][2][3][4][5][6][7][8][9] by many researchers. In the recent years, some relevant methods have been proposed for mining high utility itemsets from transaction databases.…”

Section: Related Workmentioning

confidence: 99%

An Improved UP-Growth High Utility Itemset Mining

Adinarayanareddy¹,

Rao²,

Prasad³

2012

IJCA

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Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure, viz., UP-Tree, maintains the information of transactions and itemsets, facilitate the mining performance and avoid scanning original database repeatedly. In this paper, UP-Tree (Utility Pattern Tree) is adopted, which scans database only twice to obtain candidate items and manage them in an efficient data structured way. Applying UP-Tree to the UP-Growth takes more execution time for Phase II. Hence this paper presents modified algorithm aiming to reduce the execution time by effectively identifying high utility itemsets.

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“…Utility mining [5,29,30] was proposed to solve the above mentioned problem by considering the factors like cost, profit or other factors of users" interest. Thus the issue of high utility itemsets mining is raised and many studies [4,9,17,19,20,25,27] have addressed this problem. Liu, Liao & Choudhary [19,20] proposed the two phase utility mining algorithm for efficiently extracting all high utility itemsets based on the downward closure property.…”

Section: Introductionmentioning

confidence: 99%

Recommendation System for High Utility Itemsets over Incremental Dataset

mozhi¹

2015

IJIRSET

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ABSTRACT:Mining high utility itemsets has gained much significance in the recent years. When the data arrives sporadically, incremental and interactive utility mining approaches can be adopted to handle users" dynamic environmental needs and avoid redundancies, using previous data structures and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests high utility itemsets over dynamic datasets for a location prediction strategy to predict users" trajectories using the Fast Update Utility Pattern Tree (FUUP) approach. Through comprehensive evaluations by experiments this scheme has shown to deliver excellent performance.

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Efficient Mining of High Utility Itemsets from Large Datasets

Cited by 92 publications

References 8 publications

An Efficient Discovery of High Utility Item Setsfrom Large Database

An Efficient Discovery of High Utility Item Setsfrom Large Database

An Improved UP-Growth High Utility Itemset Mining

Recommendation System for High Utility Itemsets over Incremental Dataset

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