Evaluating Top-k Approximate Patterns via Text Clustering

Claudio Lucchese, HPC Lab., ISTI-CNR, Pisa, Italy
Salvatore Orlando, DAIS - Universit Ca’ Foscari Venezia, Italy
Raffaele Perego, HPC Lab., ISTI-CNR, Pisa, Italy

May 20 2016

Accepted at DAWAK ’16: 18th International Conference on Data Warehousing and Knowledge Discovery [1].

Abstract. 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.

References

[1]   Claudio Lucchese, Salvatore Orlando, and Raffaele Perego. Evaluating top-k approximate patterns via text clustering. In DAWAK ’16: 18th International Conference on Data Warehousing and Knowledge Discovery, 2015.

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