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    STUDIA INFORMATICA - Issue no. 1 / 2005  
         
  Article:   LARGE CANDIDATE BRANCH-BASED METHOD FOR MINING CONCURRENT BRANCH PATTERNS.

Authors:  JING LU, OSEI ADJEI, WEIRU CHEN, FIAZ HUSSAIN, CĂLIN ENĂCHESCU, DUMITRU RĂDOIU.
 
       
         
  Abstract:  This paper presents a novel data mining technique, known as Post Sequential Patterns Mining. The technique can be used to discover structural patterns that are composed of sequential patterns, branch pat- terns or iterative patterns. The concurrent branch pattern is one of the main forms of structural patterns and plays an important role in event-based data modelling. To discover concurrent branch patterns eficiently, a concurrent group is defined and this is used roughly to discover candidate branch pat- terns. Our technique accomplishes this by using an algorithm to determine concurrent branch patterns given a customer database. The computation of the support for such patterns is also discussed.  
         
     
         
         
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