Mining erasable itemsets with subset and superset itemset constraints

Erasable itemset (El) mining, a branch of pattern mining, helps managers to establish new plans for the development of new products. Although the problem of mining Els was first proposed in 2009, many efficient algorithms for mining these have since been developed. However, these algorithms usually require a lot of time and memory usage. In reality, users only need a small number of Els which satisfy a particular condition. Having this observation in mind, in this study we develop an efficient algorithm for mining Els with subset and superset itemset constraints (C-0 subset of X subset of C-1). Firstly, based on the MEI (Mining Erasable Itemsets) algorithm, we present the MEIC (Mining Erasable Itemsets with subset and superset itemset Constraints) algorithm in which each El is checked with regard to the constraints before being added to the results. Next, two propositions supporting quick pruning of nodes that do not satisfy the constraints are established. Based on these, we propose an efficient algorithm for mining Els with subset and superset itemset constraints (called pMEIC - p: pruning). The experimental results show that pMEIC outperforms MEIC in terms of mining time and memory usage. (C) 2016 Elsevier Ltd. All rights reserved.

Title:	Mining erasable itemsets with subset and superset itemset constraints
Authors:	Vo Bay Le Tuong Nguyen Giang
Keywords:	Data mining Erasable itemset Subset and superset itemset constraint Pruning techniques
Issue Date:	2017
Publisher:	PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
Citation:	ISIKNOWLEDGE
Abstract:	Erasable itemset (El) mining, a branch of pattern mining, helps managers to establish new plans for the development of new products. Although the problem of mining Els was first proposed in 2009, many efficient algorithms for mining these have since been developed. However, these algorithms usually require a lot of time and memory usage. In reality, users only need a small number of Els which satisfy a particular condition. Having this observation in mind, in this study we develop an efficient algorithm for mining Els with subset and superset itemset constraints (C-0 subset of X subset of C-1). Firstly, based on the MEI (Mining Erasable Itemsets) algorithm, we present the MEIC (Mining Erasable Itemsets with subset and superset itemset Constraints) algorithm in which each El is checked with regard to the constraints before being added to the results. Next, two propositions supporting quick pruning of nodes that do not satisfy the constraints are established. Based on these, we propose an efficient algorithm for mining Els with subset and superset itemset constraints (called pMEIC - p: pruning). The experimental results show that pMEIC outperforms MEIC in terms of mining time and memory usage. (C) 2016 Elsevier Ltd. All rights reserved.
Description:	TNS06983 ; EXPERT SYSTEMS WITH APPLICATIONS Volume: 69 Pages: 50-61 Published: MAR 1 2017
URI:	http://repository.vnu.edu.vn/handle/VNU_123/28438 http://www.sciencedirect.com/science/article/pii/S0957417416305590
ISSN:	0957-4174 1873-6793
Appears in Collections:	Bài báo của ĐHQGHN trong Web of Science