Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
A statistical interestingness measures for XML based association rules
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
The long and the short of it: summarising event sequences with serial episodes
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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One of the major problems in frequent pattern mining is the explosion of the number of results, making it difficult to identify the interesting frequent patterns. In a recent paper [7] we have shown that an MDL-based approach gives a dramatic reduction of the number of frequent item sets to consider. Here we show that MDL gives similarly good reductions for frequent patterns on other types of data, viz., on sequences and trees. Reductions of two to three orders of magnitude are easily attained on data sets from the web-mining field.