Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Comparison of interestingness functions for learning web usage patterns
Proceedings of the eleventh international conference on Information and knowledge management
Mining hybrid sequential patterns and sequential rules
Information Systems
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Efficient Data Mining Technique for Discovering Interesting Sequential Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Managing Interesting Rules in Sequence Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Measuring the interestingness of discovered knowledge: A principled approach
Intelligent Data Analysis
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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.