Abstract
One of the main issues in the rule/pattern mining is of measuring the interestingness of a pattern. The interestingness has been evaluated previously in literature using several approaches for association as well as for sequential mining. These approaches generally view a sequence as another form of association for computations and understanding. But, by doing so, a sequence might not be fully understood for its statistical significance such as dependence and applicability. This paper proposes a new framework to study sequences’ interestingness. It suggests two kinds of Markov processes, namely Bayesian networks, to represent the sequential patterns. The patterns are studied for statistical dependencies in order to rank the sequential patterns interestingness. This procedure is very shown when the domain knowledge is not easily accessible.
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Vellaisamy, K., Li, J. (2006). Bayesian Approaches to Ranking Sequential Patterns Interestingness. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_27
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DOI: https://doi.org/10.1007/978-3-540-36668-3_27
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