scholarly journals A GA-Based Approach to Hide Sensitive High Utility Itemsets

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chun-Wei Lin ◽  
Tzung-Pei Hong ◽  
Jia-Wei Wong ◽  
Guo-Cheng Lan ◽  
Wen-Yang Lin
Keyword(s):  
High Risk ◽  
Evaluation Process ◽  
Information Loss ◽  
Evaluation Function ◽  
Mining Method ◽  
Utility Mining ◽  
High Utility ◽  
High Utility Itemsets ◽  
Downward Closure ◽  
The Cost

A GA-based privacy preserving utility mining method is proposed to find appropriate transactions to be inserted into the database for hiding sensitive high utility itemsets. It maintains the low information loss while providing information to the data demanders and protects the high-risk information in the database. A flexible evaluation function with three factors is designed in the proposed approach to evaluate whether the processed transactions are required to be inserted. Three different weights are, respectively, assigned to the three factors according to users. Moreover, the downward closure property and the prelarge concept are adopted in the proposed approach to reduce the cost of rescanning database, thus speeding up the evaluation process of chromosomes.

A Systematic Survey on High Utility Itemset Mining

2019 ◽  
Vol 18 (04) ◽  
pp. 1113-1185 ◽  
Author(s):  
Bahareh Rahmati ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Data Structures ◽  
Search Space ◽  
Frequent Itemset ◽  
Itemset Mining ◽  
Efficient Data ◽  
Average Utility ◽  
High Utility ◽  
High Utility Itemsets ◽  
Downward Closure ◽  
Efficient Data Structures

High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules. Downward closure property, which causes significant pruning in frequent itemset mining, is not established in the utility of itemsets and so the mining problem will require alternative solutions to reduce its search space and to enhance its efficiency. Using an anti-monotonic upper bound of the utility function and exploiting efficient data structures for storing and compacting the dataset to perform efficient pruning strategies are the main solutions to address high utility itemset mining problem. Different mining methods and techniques have attempted to improve performance of extracting high utility itemsets and their several variants, including high-average utility itemsets, top-k high utility itemsets, and high utility itemsets with negative values, using more efficient data structures, more appropriate anti-monotonic upper bounds, and stronger pruning strategies. This paper aims to represent a comprehensive systematic review for high utility itemset mining techniques and to classify them based on their problem-solving approaches.

scholarly journals Optimized High-Utility Itemsets Mining for Effective Association Mining Paper

2017 ◽  
Vol 7 (5) ◽  
pp. 2911 ◽  
Author(s):  
K Rajendra Prasad
Keyword(s):  
Association Rule ◽  
Optimal Number ◽  
Large Datasets ◽  
Association Mining ◽  
Rule Mining ◽  
Utility Mining ◽  
Utility Factor ◽  
High Utility ◽  
High Utility Itemsets ◽  
Growth Methods

Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets.

Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining

2016 ◽  
Vol 55 ◽  
pp. 269-284 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Tsu-Yang Wu ◽  
Philippe Fournier-Viger ◽  
Guo Lin ◽  
Justin Zhan ◽  
...  
Keyword(s):  
Fast Algorithms ◽  
Privacy Preserving ◽  
Utility Mining ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals An efficient HUIBA Algorithm for Mining High Utility Itemset

2021 ◽  
Vol 23 (11) ◽  
pp. 566-573
Author(s):  
M.S. Bhuvaneswari ◽  
◽  
N. Balaganesh ◽  
Keyword(s):  
User Preferences ◽  
Convergence Speed ◽  
Utility Mining ◽  
Transaction Database ◽  
High Utility ◽  
High Utility Itemsets

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.

scholarly journals Top-k High Utility Itemset Mining Framework by using TKU Along with TKO Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 1377-1380
Keyword(s):  
Sequence Mining ◽  
Utility Mining ◽  
Itemset Mining ◽  
Mining Technique ◽  
Problematic Issue ◽  
Ideal Number ◽  
High Utility ◽  
High Utility Itemsets ◽  
Farthest Point

High utility itemsets (HUIs) mining be developing point within in sequence mining, which alludes toward discovering the entire itemsets contains utility assembling consumer verified slightest utility farthest point minutil. In any case, setting minutil fittingly is a problematic issue for customers. When in doubt, discovering reasonable smallest amount utility farthest point through testing be repetitive methodology intended for customers. On off chance to minutil be place excessively small, such countless HUIs determination exist made, which may make mining technique survive uncommonly incompetent. Additional supply taking place, condition minutil be place unnecessarily, we do not find any HUI. Within term document, we deal with the exceeding concerns through planning an additional composition in favor of top-k high utility itemset mining, where k be an ideal number of HUIs toward mined. Two arranges about viable counts named TKU (mining Top-K Utility itemsets) plus TKO (mining Top-K utility itemsets within one phase) be planned in favor of mining that itemsets not including require toward position minutil. We provide assistant relationship about two estimations among trades on top of central focuses along with obstacles. Precise appraisals lying on together authentic as well as made datasets shows introduction about proposed computations be close to the perfect case about top tier utility mining estimations.

scholarly journals Depth Impurity Pruned Strategies for Extracting High Utility Itemsets

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.

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

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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