Efficient Algorithms for Mining Top-K High Utility Itemsets

Tseng, Vincent S.; Wu, Chengwei; Fournier-Viger, Philippe; Yu, Philip S.

doi:10.1109/tkde.2015.2458860

Cited by 230 publications

(116 citation statements)

References 35 publications

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…Negative unit profits of items are addressed in [15,16]. To avoid difficulties in setting a proper utility threshold, in [17][18][19] attempts were done to mine a set of itemsets with the highest utility. Podpecan [20] and Sugunadevi [21] proposed to discover high utility-frequent itemsets based on consideration of utility and frequency of occurrence.…”

Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

Mining High Utility Itemsets with Regular Occurrence

et al. 2016

View full text Add to dashboard Cite

Abstract. High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints.

show abstract

Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

Mining High Utility Itemsets with Regular Occurrence

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Itemset List Is Generated As Follows: [9], [10] is one of the efficient algorithms to generate high utility itemsets depending on construction of a global UP-Tree. The tree maintains the information about the itemsets and their utilities.…”

Section: A Phase I: Discover High Transaction Weighted Utilitymentioning

confidence: 99%

A Better Approach for Mining High Utility Itemset from Transactional Databases

Zawar¹,

Barakare²

2016

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

Data Mining can be defined as an activity that extracts some new nontrivial information contained in large databases. Traditional data mining techniques have focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases. Also termed as frequent itemset mining, these techniques were based on the rationale that itemsets which appear more frequently must be of more importance to the user from the business perspective .In this thesis we throw light upon an emerging area called Utility Mining which not only considers the frequency of the itemsets but also considers the utility associated with the itemsets. The term utility refers to the importance or the usefulness of the appearance of the itemset in transactions quantified in terms like profit, sales or any other user preferences. In High Utility Itemset Mining the objective is to identify itemsets that have utility values above a given utility threshold. In existing system some high utility itemset mining algorithms such as Two-Phase, UPGrowth have been proposed. But there is problem like it requires more execution time and it uses more memory. The new method is memory efficient technique for mining high utility itemsets from transactional databases. This technique requires less memory space and execution time than existing algorithms.

show abstract

“…Such a large number of HTWUI"s will degrade the mining performance in phase I substantially in terms of execution time and memory consumption. As advancement, Tseng, Wu, Shie & Yu [27] proposed UP-Growth for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. Correspondingly, a compact tree structure, called UP-Tree (Utility Pattern Tree), was designed to maintain the important information of the transaction database related to the utility patterns.An incremental mining algorithm FUUP tree [31] for efficiently mining high utility itemsets is proposed to handle dynamic datasets.…”

Section: A Mining High Utility Itemsetsmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

Recommendation System for High Utility Itemsets over Incremental Dataset

mozhi¹

2015

IJIRSET

View full text Add to dashboard Cite

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.

show abstract

Efficient Algorithms for Mining Top-K High Utility Itemsets

Cited by 230 publications

References 35 publications

Mining High Utility Itemsets with Regular Occurrence

Mining High Utility Itemsets with Regular Occurrence

A Better Approach for Mining High Utility Itemset from Transactional Databases

Recommendation System for High Utility Itemsets over Incremental Dataset

Contact Info

Product

Resources

About