AN EFFECTIVE STRATEGY TO HIDE SENSITIVE HIGH UTILITY ITEMSETS IN TRANSACTION DATABASE

Utility Mining is to spot the itemsets with highest utilities, by considering profit, quantity, cost or other user preferences. Mining High Utility itemsets from a transaction database is to seek out itemsets that have utility above a user-specified threshold. Bio inspired algorithm is extremely efficient for mining High Utility Itemset(HUI), but it will not find all HUI in the database and the quality is poor within the number of discovered HUI. A replacement framework using BA algorithm is proposed to rectify this issue. The proposed algorithm is more efficient in terms of quality and convergence speed when put next to other algorithms.

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A hybrid framework for mining high-utility itemsets in a sparse transaction database

Applied Intelligence ◽

10.1007/s10489-017-0932-1 ◽

2017 ◽

Vol 47 (3) ◽

pp. 809-827 ◽

Cited By ~ 8

Author(s):

Siddharth Dawar ◽

Vikram Goyal ◽

Debajyoti Bera

Keyword(s):

Hybrid Framework ◽

Transaction Database ◽

High Utility ◽

High Utility Itemsets

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An Improved Algorithm to Protect Sensitive High Utility Itemsets in Transaction Database

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Nature of Computation and Communication ◽

10.1007/978-3-030-92942-8_9 ◽

2021 ◽

pp. 95-107

Author(s):

Nguyen Khac Chien ◽

Dang Thi Kim Trang

Keyword(s):

Transaction Database ◽

High Utility ◽

High Utility Itemsets ◽

Improved Algorithm

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Parallel Mining for High Utility Itemsets Mining by Efficient Data Structure

Research and Development on Information and Communication Technology ◽

10.32913/rd-ict.vol3.no14.519 ◽

2017 ◽

Author(s):

Nguyen Manh Hung ◽

Dau Hai Phong

Keyword(s):

Data Mining ◽

Data Structure ◽

Actual Number ◽

Utility Value ◽

Weighted Utility ◽

Parallel Mining ◽

Efficient Data ◽

Transaction Database ◽

High Utility ◽

High Utility Itemsets

Mining high utility itemsets in transaction database is an important task in data mining and widely applied in many areas. Recently, many algorithms have been proposed, but most algorithms for identifying high utility itemsets need to generate candidate sets by overestimating their utility and then calculating their exact utility value. Therefore, the number of candidate itemsets is much larger than the actual number of high utility itemsets. In this paper, we introduce the Retail Transaction-Weighted Utility (RTWU) structure and propose two algorithms: EAHUIMiner algorithm and PEAHUI-Miner parallel algorithm. They have been experimented and compared to the two most efficient algorithms: EFIM and FHM. Results show that our algorithm is better with sparse datasets. DOI: 10.32913/rd-ict.vol3.no14.519

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Depth Impurity Pruned Strategies for Extracting High Utility Itemsets

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.4.16747 ◽

2018 ◽

Vol 7 (3.4) ◽

pp. 52

Author(s):

K Santhi ◽

B Valarmathi ◽

T Chellatamilan

Keyword(s):

State Of The Art ◽

Database Mining ◽

Huge Amount ◽

Utility Mining ◽

Organizational Behaviour ◽

Mining Technique ◽

Transaction Database ◽

High Utility ◽

High Utility Itemsets ◽

Time And Space Complexity

Normally in a transaction database mining high utility itemsets indicates to the location of itemsets which is causing high utility like benefits. In spite of the fact that various important calculations have been proposed as of late, they bring about the issue of generating a huge amount of itemsets for mining to discover HUI. Mining is reduced by such an extended quantity as far as execution time and space complexity. When the database contains large amount of transactions, this condition may turn into mediocre. In this research paper, we account this concern by offering a state-of-the-art calculation named Depth Impurity Quality Index Pruned strategies which considers the complexity of sub-trees to more efficiently identify high-utility itemsets. It is an collection of common itemset which are used for mining and is significantly harder, inflexible. This is imputable to the absence of intrinsic organizational behaviour of HUI which could have worked. This paper suggests a high utility mining technique which make use of novel pruning approaches.The experimental outcomes disclose that the proposed method is exceptionally viable in killing unhopeful applicants in the database transactions.

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Business Intelligence for Evaluating the Intangible Benefits of On-Shelf High Utility Itemset from the Temporal Transaction Database

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8117.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 1428-1435

Keyword(s):

Research Work ◽

Decisive Role ◽

List Structure ◽

Business Decisions ◽

Utility Mining ◽

Time Period ◽

Transaction Database ◽

High Utility ◽

High Utility Itemsets ◽

Mining Algorithms

Utility Mining is the progression of identifying High Utility Itemsets (HUI’s) from enormous transaction data. Utility mining plays a decisive role in the inspection of the data or giving actionable information to help managers, sales executives, and other commercial end-users to generate versed business decisions. In the hypermarkets, the showcase period of every item in display will vary such as new products, seasonal products, and so on. Itemsets with time period are not retrieved by existing utility mining algorithms. Hence, On-Shelf Utility Mining algorithms were proposed to discover HUI’s and a general onshelf period of all items in temporal databases is considered. Research work aims to propose an algorithm called LOSUM (List On-Shelf Utility Mining) to retrieve on-shelf HUI’s from a temporal transaction database by reducing the data stores scan. The algorithm is enhanced by implementing a list structure to store utility information of every itemset. The candidate itemsets are generated from the list itself. This reduces the supplementary scan of a database. The LOSUM is compared with FOSHU using Chess, Accident, Kosarak, and Mushroom datasets. The experimental results illustrate that the LOSUM is efficient than the existing algorithm FOSHU (Fast On-Shelf High Utility itemset mining) algorithm

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