Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Discovery of Fuzzy Sequential Patterns for Fuzzy Partitions in Quantitative Attributes
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
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In real applications, transaction data typically contain quantitative attributes. Existing approaches and algorithms proposed for sequential pattern mining such as AprioriAll often assume Boolean attributes (i.e., quantitative values are simply interpreted/transformed as Boolean values). This article addresses the impact of varied quantitative attributes in sequential pattern mining. More specifically, we define alternate smart support functions for computing the support measure of candidate sequential patterns. A noticeable advantage of this work is that the proposed smart support functions can be smoothly integrated into the framework of existing sequential pattern mining algorithms. In the discussion of this article, we assume adoption of the well-known AprioriAll algorithm and discuss the incorporation of the proposed smart support functions into this framework. The expected mining results are believed better reflecting the particular interests of different user groups and thus are more satisfactory to the intended users.