Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Orlando S., Perego R. Evaluating top-K approximate patterns via text clustering. In: DaWaK 2016 - Big Data Analytics and Knowledge Discovery. 18th International Conference (Porto, Portugal, 5 - 8 September 2016). Proceedings, pp. 114 - 127. Sanjay Madria, Takahiro Hara (eds.). (Lecture Notes in Computer Science, vol. 9829). Springer, 2016.
This work investigates how approximate binary patterns can be objectively evaluated by using as a proxy measure the quality achieved by a text clustering algorithm, where the document features are derived from such patterns. Specifically, we exploit approximate patterns within the well-known FIHC (Frequent Itemset-based Hierarchical Clustering) algorithm, which was originally designed to employ exact frequent itemsets to achieve a concise and informative representation of text data. We analyze different state-of-the-art algorithms for approximate pattern mining, in particular we measure their ability in extracting patterns that well characterize the document topics in terms of the quality of clustering obtained by FIHC. Extensive and reproducible experiments, conducted on publicly available text corpora, show that approximate itemsets provide a better representation than exact ones.
URL: http://link.springer.com/chapter/10.1007/978-3-319-43946-4_8
DOI: 10.1007/978-3-319-43946-4_8
Subject Pattern Mining
H.2.8 DATABASE MANAGEMENT. Database Applications. Data Mining

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