A New Approach for Association Rule Mining and Bi-clustering using Formal Concept Analysis

Abstract : Association rule mining and bi-clustering are data mining tasks that have become very popular in many application domains, particularly in bioinformatics. However, to our knowledge, no algorithm was introduced for performing these two tasks in one process. We propose a new approach called FIST for extracting bases of extended association rules and conceptual bi-clusters conjointly. This approach is based on the frequent closed itemsets framework and requires a unique scan of the database. It uses a new suffix tree based data structure to reduce memory usage and improve the extraction efficiency, allowing parallel processing of the tree branches. Experiments conducted to assess its applicability to very large datasets show that FIST memory requirements and execution times are in most cases equivalent to frequent closed itemsets based algorithms and lower than frequent itemsets based algorithms.
Type de document :
Communication dans un congrès
MLDM'2012 International Conference on Machine Learning and Data Mining, Jul 2012, Berlin, Germany. Springer, Lecture Notes in Artificial Intelligence 7376, pp.86-101, <http://www.mldm.de/>. <10.1007/978-3-642-31537-4_8>


https://hal.archives-ouvertes.fr/hal-01330095
Contributeur : Nicolas Pasquier <>
Soumis le : jeudi 9 juin 2016 - 22:53:43
Dernière modification le : vendredi 26 août 2016 - 10:59:25

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Kartick Chandra Mondal, Nicolas Pasquier, Mukhopadhyay Anirban, Ujjwal Maulik, Bandhopadyay Sanghamitra. A New Approach for Association Rule Mining and Bi-clustering using Formal Concept Analysis. MLDM'2012 International Conference on Machine Learning and Data Mining, Jul 2012, Berlin, Germany. Springer, Lecture Notes in Artificial Intelligence 7376, pp.86-101, <http://www.mldm.de/>. <10.1007/978-3-642-31537-4_8>. <hal-01330095>

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