Extracting Mutually Dependent Multisets

In this paper, we extend mutually dependent patterns as itemsets introduced by Ma and Hellerstein (2001) to mutually dependent multisets allowing two or more occurrences of the same items. Then, by improving the algorithm to extract all of the mutually dependent patterns based on Apriori with maintaining itemsets and their supports, we design the algorithm to extract all of the mutually dependent multisets based on AprioriTid with traversing a database just once and maintaining both multisets and their tail occurrences but without computing overall multiplicity of items in multisets. Finally, we give experimental results to apply the algorithm to both real data as antibiograms consisting of a date, a patient id, a detected bacterium, and so on and artificial data obtained by repeating items in transaction data.

[1]  M. H. Margahny,et al.  FAST ALGORITHM FOR MINING ASSOCIATION RULES , 2014 .

[2]  Mohammed J. Zaki,et al.  Data Mining And Analysis , 2016 .

[3]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[4]  Tao Li,et al.  Event Mining: Algorithms and Applications , 2015 .

[5]  Joseph L. Hellerstein,et al.  Mining mutually dependent patterns , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[6]  Kouichi Hirata,et al.  Extracting Propagation Patterns from Bacterial Culture Data in Medical Facility , 2015, JSAI-isAI Workshops.

[7]  Edward Omiecinski,et al.  Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..

[8]  Hui Xiong,et al.  Hyperclique pattern discovery , 2006, Data Mining and Knowledge Discovery.