PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Perego R., Orlando S. A generative pattern model for mining binary datasets. In: SAC 2010 - 25th ACM Symposium On Applied Computing (Crans Montana, Switzerland, March 22-26 2010). Abstract, pp. 1109 - 1110. ACM, 2010.
 
 
Abstract
(English)
In many application fields, huge binary datasets modeling real life-phenomena are daily produced. These datasets record observations of some events, and people are often interested in mining them in order to recognize recurrent patterns. However, the discovery of the most important patterns is very challenging. For example, these patterns may overlap, or be related only to a particular subset of the observations. Finally, the mining can be hindered by the presence of noise. In this paper, we introduce a generative pattern model, and an associated cost model for evaluating the goodness of the set of patterns extracted from a binary dataset. We pro- pose an efficient algorithm, named GPM, for the discovery of the most relevant patterns according to the model. We show that the proposed model generalizes other approaches and supports the discovery of high quality patterns.
URL: http://portal.acm.org/citation.cfm?id=1774320&CFID=113749544&CFTOKEN=56747035
DOI: 10.1145/1774088.1774320
Subject Frequent pattern mining
H.2.8 Database Applications. Data mining


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