MuTIny: A Multi-Time Interval Pattern Discovery Approach To Preserve The Temporal Information In Between
Finding trends, patterns, and relationships among patterns are very relevant tasks when dealing with time-oriented data and information. However, most of the proposed methods have a sequence of events as outcome, lacking either any knowledge about the intervals between them or about after how much time a particular pattern will reoccur. We present MuTIny, a novel approach extending the I-Apriori algorithm, which is able to discover so called multi-time interval patterns and we describe how it can be customized according to users’ needs. Moreover, a real world example illustrates its usefulness.
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IADIS European Conference on Data Mining (ECDM 2010)
International Association for Development of the Information Society