Low-Entropy Set Selection.

Most pattern discovery algorithms easily generate very large numbers of patterns, making the results impossible to understand and hard to use. Recently, the problem of instead selecting a small subset of informative patterns from a large collection of patterns has attracted a lot of interest. In this paper we present a succinct way of representing data on the basis of itemsets that identify strong interactions. This new approach, LESS, provides a more powerful and more general technique to data description than existing approaches. Low-entropy sets consider the data symmetrically and as such identify strong interactions between attributes, not just between items that are present. Selection of these patterns is executed through the MDL-criterion. This results in only a handful of sets that together form a compact lossless description of the data. By using entropy-based elements for the data description, we can successfully apply the maximum likelihood principle to locally cover the dat...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Hannes Heikinheimo, Jilles Vreeken, Arno Siebes, Heikki Mannila
• Keywords: Computer Science, Data Description, SDM 2009, Small Pattern Sets, Strong Interactions