Surprisingness – A Novel Objective Interestingness Measure in Hypergraph Pattern Mining for Unsupervised Common Sense Learning

Pattern mining usually results in huge amounts of patterns, among which only small percentages are interesting. In this paper, Surprisingness (including Surpringness_I and Surpringness_II) is proposed as an innovative objective multivariate interestingness measure for automatically identifying interesting patterns from a large quantity of patterns. Surprisingness is applicable in unstructured or semi-structured, multi-domain or mixed-domain data compared to existing measures. An experiment has been conducted enabling unsupervised learning of common sense, interesting patterns and exceptions from Wikipedia extracted data of random mixed domains (represented as a directed labeled hypergraphs), using the combinations of support and Surpringness.