Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
IEEE Transactions on Knowledge and Data Engineering
Mining non-redundant time-gap sequential patterns
Applied Intelligence
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Mining sequential patterns is to discover sequential purchasing behaviors for most of the customers from a large amount of customer transactions. An example of such a pattern is that most of the customers purchased item B after purchasing item A, and then they purchased item C after using item B. The manager can use this information to promote item B and item C when a customer purchased item A and item B, respectively. However, the manager cannot know what time the customers will need these products if we only discover the sequential patterns without any extra information. In this paper, we develop a new algorithm to discover not only the sequential patterns but also the time interval between any two items in the pattern. We call this information the time-gap sequential patterns. An example of time-gap sequential pattern is that most customers purchased item A, and then they bought item B after m to n days, and then after p to q days, they bought item C. When a customer bought item A, the information about item B can be sent to this customer after m to n days, that is, we can provide the product information in which the customer is interested on the appropriate date.