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Mining Unexpected Sequential Patterns and Implication Rules (2009)
BibTeX
@MISC{Li09miningunexpected,
author = {Dong (haoyuan Li and Anne Laurent and Pascal Poncelet},
title = {Mining Unexpected Sequential Patterns and Implication Rules},
year = {2009}
}
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Abstract
As common criteria in data mining methods, the frequency-based interestingness measures provide a statistical view of the correlation in the data, such as sequential patterns. However, when we consider domain knowledge within the mining process, the unexpected information that contradicts existing knowledge on the data has never less importance than the regularly frequent information. For this purpose, we present the approach USER for mining unexpected sequential rules in sequence databases. We propose a belief-driven formalization of the unexpectedness contained in sequential data, with which we propose 3 forms of unexpected sequences. We further propose the notion of unexpected sequential patterns and implication rules for determining the structures and implications of the unexpectedness. The experimental results on various types of data sets show the usefulness and effectiveness of our approach.
Keyphrases
sequential pattern implication rule approach user domain knowledge mining unexpected sequential rule unexpected sequential pattern sequence database data set unexpected information mining process frequency-based interestingness measure regularly frequent information common criterion sequential data belief-driven formalization unexpected sequence statistical view experimental result various type data mining method
