PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Perego R., Orlando S. Supervised evaluation of top-k itemset mining algorithms. In: DaWaK 2015 - Big Data Analytics and Knowledge Discovery. 17th International Conference (Valencia, Spain, 01 - 04 September 2015). Proceedings, pp. 82 - 94. Sanjay Madria, Takahiro Hara (eds.). (Lecture Notes in Computer Science, vol. 9263). Springer, 2015.
 
 
Abstract
(English)
A major mining task for binary matrixes is the extraction of approximate top-k patterns that are able to concisely describe the input data. The top-k pattern discovery problem is commonly stated as an optimization one, where the goal is to minimize a given cost function, e.g., the accuracy of the data description. In this work, we review several greedy state-of-the-art algorithms, namely Asso, Hyper+, and PaNDa+, and propose a methodology to compare the patterns extracted. In evaluating the set of mined patterns, we aim at overcoming the usual assessment methodology, which only measures the given cost function to minimize. Thus, we evaluate how good are the models/patterns extracted in unveiling supervised knowledge on the data. To this end, we test algorithms and diverse cost functions on sev- eral datasets from the UCI repository. As contribution, we show that PaNDa+ performs best in the majority of the cases, since the classi- fiers built over the mined patterns used as dataset features are the most accurate.
URL: http://link.springer.com/chapter/10.1007%2F978-3-319-22729-0_7
DOI: 10.1007/978-3-319-22729-0_7
Subject Approximate patterns
H.2.8 Database Applications. Data mining


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