Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

Download Full-text

MINING ESSENTIAL RULES USING FREQUENT CLOSED ITEMSETS LATTICE

Science and Technology Development Journal ◽

10.32508/stdj.v12i11.2311 ◽

2009 ◽

Vol 12 (11) ◽

pp. 49-56

Author(s):

Bac Hoai Le ◽

Bay Dinh Vo

Keyword(s):

Association Rules ◽

Frequent Itemsets ◽

Suitable Method ◽

Mining Method ◽

Parent Child Relationship ◽

Left Hand ◽

Child Relationship ◽

Closed Itemsets ◽

The Cost ◽

Parent Child

In traditional mining of association rules, finding all association rules from databases that satisfy minSup and minConf faces with some problems in case of the number of frequent itemsets is large. Thus, it is necessary to have a suitable method for mining fewer rules but they still embrace all rules of traditional mining method. One of the approaches that is the mining method of essential rules: it only keeps the rule that its left hand side is minimal and its right side is maximal (follow in parent-child relationship). In this paper, we propose a new algorithm for mining the essential rules from the frequent closed itemsets lattice to reduce the time of mining rules. We use the parent-child relationship in lattice to reduce the cost of considering parent-child relationship and lead to reduce the time of mining rules.

Download Full-text

GRG: an efficient method for association rules mining on frequent closed itemsets

Proceedings of the 2003 IEEE International Symposium on Intelligent Control ISIC-03 ◽

10.1109/isic.2003.1254748 ◽

2003 ◽

Cited By ~ 1

Author(s):

Li Li ◽

Donghai Zhai ◽

Fan Jin

Keyword(s):

Association Rules ◽

Efficient Method ◽

Association Rules Mining ◽

Closed Itemsets

Download Full-text

Deriving Concise Association Rules Efficiently while Extracting Closed Itemsets in Depth-first Manner

Journal of Convergence Information Technology ◽

10.4156/jcit.vol7.issue10.8 ◽

2012 ◽

Vol 7 (10) ◽

pp. 68-76 ◽

Cited By ~ 1

Author(s):

Bingzheng Wang ◽

Huixin Wu ◽

Jiming Ma

Keyword(s):

Association Rules ◽

Closed Itemsets

Download Full-text

MINING NON-REDUNDANT ASSOCIATION RULES BASED ON CONCISE BASES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005600 ◽

2007 ◽

Vol 21 (04) ◽

pp. 659-675 ◽

Cited By ~ 11

Author(s):

YUE XU ◽

YUEFENG LI

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Association Rule Mining ◽

Rule Mining ◽

Mining Community ◽

Rule Set ◽

Closed Itemsets ◽

Minimal Generators

Association rule mining has many achievements in the area of knowledge discovery. However, the quality of the extracted association rules has not drawn adequate attention from researchers in data mining community. One big concern with the quality of association rule mining is the size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant, thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a reliable exact association rule basis from which more concise nonredundant rules can be extracted. We prove that the redundancy eliminated using the proposed reliable association rule basis does not reduce the belief to the extracted rules. Moreover, this paper proposes a level wise approach for efficiently extracting closed itemsets and minimal generators — a key issue in closure based association rule mining.

Download Full-text

Mining Method for Weighted Concise Association Rules Based on Closed Itemsets under Weighted Support Framework

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.236-237.326 ◽

2012 ◽

Vol 236-237 ◽

pp. 326-333

Author(s):

Zhi Cheng Qu ◽

Meng Ye ◽

Bin Jiang

Keyword(s):

Association Rules ◽

Association Rule ◽

Search Space ◽

The Other ◽

Mining Method ◽

Specific Technique ◽

Other Hand ◽

Closed Itemsets ◽

Transaction Database ◽

Rule Representation

Association rules tell us interesting relationships between different items in transaction database. But traditional association rule has two disadvantages. Firstly it assumes every two items have same significance in database, which is unreasonable in many real applications and usually leads to incorrect results. On the other hand, traditional association rule representation contains too much redundancy which makes it difficult to be mined and used. This paper addresses the problem of mining weighted concise association rules based on closed itemsets under weighted support-significant framework, in which each item with different significance is assigned different weight. Through exploiting specific technique, the proposed algorithm can mine all weighted concise association rules while duplicate weighted itemset search space is pruned. As illustrated in experiments, the proposed method leads to good results and achieves good performance.

Download Full-text