The Comparative of Boolean Algebra Compress and Apriori Rule Techniques for New Theoretic Association Rule Mining Model

2011 ◽  
Vol 3 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Somboon Anekritmongkol ◽  
Kulthon Kasamsan
Keyword(s):  
Boolean Algebra ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Mining Model

Mining for Mutually Exclusive Items in Transaction Databases

2008 ◽  
pp. 2993-3004
Author(s):  
George Tzanis ◽  
Christos Berberidis
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Review Of The Literature ◽  
Rule Mining ◽  
Transaction Data ◽  
Mining Algorithm ◽  
Concise Review ◽  
Evaluation Metric ◽  
Mining Model

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

Mining for Mutually Exclusive Items in Transaction Databases

2009 ◽  
pp. 2192-2203
Author(s):  
George Tzanis ◽  
Christos Berberidis
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Review Of The Literature ◽  
Rule Mining ◽  
Transaction Data ◽  
Mining Algorithm ◽  
Concise Review ◽  
Evaluation Metric ◽  
Mining Model

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

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