Knowledge discovery interestingness measures based on unexpectedness

Knowledge discovery methods often discover a large number of patterns. Although this can be considered of interest, it certainly presents considerable challenges too. Indeed, this set of patterns often contains lots of uninteresting patterns that risk overwhelming the data miner. In addition, a single interesting pattern can be discovered in a multitude of tiny variations that for all practical purposes are redundant. These issues are referred to as the pattern explosion problem. They lie at the basis of much recent research attempting to quantify interestingness and redundancy between patterns, with the purpose of filtering down a large pattern set to an interesting and compact subset. Many diverse approaches to interestingness and corresponding interestingness measures (IMs) have been proposed in the literature. Some of them, named objective IMs, define interestingness only based on objective criteria of the pattern and data at hand. Subjective IMs additionally depend on the user's prior knowledge about the dataset. Formalizing unexpectedness is probably the most common approach for defining subjective IMs, where a pattern is deemed unexpected if it contradicts the user's expectations about the dataset. Such subjective IMs based on unexpectedness form the focus of this paper. We categorize measures based on unexpectedness into two major subgroups, namely, syntactical and probabilistic approaches. Based on this distinction, we survey different methods for assessing the unexpectedness of patterns with a special focus on frequent itemsets, tiles, association rules, and classification rules. © 2012 Wiley Periodicals, Inc.

This article is categorized under:

• Algorithmic Development > Association Rules
• Algorithmic Development > Statistics
• Technologies > Statistical Fundamentals