Itemset mining is a well-known exploratory data mining technique used to discover
interesting correlations hidden in a data collection. Since it supports different targeted …

Graph-based summarization entails extracting a worthwhile subset of sentences from a
collection of textual documents by using a graph-based model to represent the correlations …

In this paper, we solve the following data summarization problem: given a multi-dimensional
data set augmented with a binary attribute, how can we construct an interpretable and …

The development of platforms and techniques for emerging Big Data and Machine Learning
applications requires the availability of real‐life datasets. A possible solution is to synthesize …

We present smart drill-down, an operator for interactively exploring a relational table to
discover and summarize “interesting” groups of tuples. Each group of tuples is described by …

A number of studies, theoretical, empirical, or both, have been conducted to provide insight
into the properties and behavior of interestingness measures for association rule mining …

Automatic text summarizers can reduce the time required to read lengthy text documents by
extracting the most important parts. Multi-document summarizers should produce a summary …

Mining small, useful, and high-quality sets of patterns has recently become an important
topic in data mining. The standard approach is to first mine many candidates, and then to …

Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration,
the selection of patterns constitutes a major challenge. The Minimum Description Length …

Sentence-based summarization aims at extracting concise summaries of collections of
textual documents. Summaries consist of a worthwhile subset of document sentences. The …