Evaluating Top-K Approximate Patterns via Text Clustering

  • Claudio Lucchese
  • Salvatore Orlando
  • Raffaele Perego
Conference paper

DOI: 10.1007/978-3-319-43946-4_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9829)
Cite this paper as:
Lucchese C., Orlando S., Perego R. (2016) Evaluating Top-K Approximate Patterns via Text Clustering. In: Madria S., Hara T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science, vol 9829. Springer, Cham

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.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Claudio Lucchese
    • 1
  • Salvatore Orlando
    • 1
    • 2
  • Raffaele Perego
    • 1
  1. 1.ISTI-CNRPisaItaly
  2. 2.DAIS - Università Ca’ Foscari VeneziaVeniceItaly

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