Mining Frequent Patterns with Multiple Item Support Thresholds in Tourism Information Databases - Technologies and Applications of Artificial Intelligence - page 98

5 Conclusions

This paper has proposed how to mine frequent pattern containing both frequent and
rare items with multiple min_sups and presented a maintenance method for automatic
min_siup specifying without rescanning database. We have implemented the im-
proved CFP-growth++ method. By conducting experiments on tourism information
dataset, the effectiveness of the maintenance method for automatic min_sup specify-
ing is shown experimentally and practically.
There remain some problems that are worth studying in the future. First, the MIS-
tree maintenance problem will be considered. Since the database is updated conti-
nuously, how to maintain the MIS-tree structure is an interesting problem. Second, we
are planning to use the concept of frequent closed pattern to make the mining process
efficiently.

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