Small is beautiful: discovering the minimal set of unexpected patterns

A drawback of most traditional data mining methods is that they do not leverage prior knowledge of users. In many business settings, managers and analysts have significant intuition based on several years of experience. In prior work [11, 12] we proposed methods that could discover unexpected patterns in data by using this domain knowledge in a systematic manner. In this paper we continue our focus on discovering unexpected patterns and propose new methods for discovering a minimal set of unexpected patterns that discover orders of magnitude fewer patterns and yet retain most of the truly interesting ones. We demonstrate the strengths of this approach experimentally using a case study application in a marketing domain.

• Type: Conference
• Year: 2000
• Where: KDD
• Authors: Balaji Padmanabhan, Alexander Tuzhilin
• Keywords: Data Mining, Data Mining Methods, KDD 2000, Magnitude Fewer Patterns, Unexpected Patterns