Evaluating Top-K Approximate Patterns via Text Clustering

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.