Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
On benchmarking frequent itemset mining algorithms: from measurement to analysis
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Summarizing itemset patterns using probabilistic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying Follow-Correlation Itemset-Pairs
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Reducing the Frequent Pattern Set
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Compression picks item sets that matter
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Mining frequent patterns is a major topic in data mining research, resulting in many seminal papers and algorithms on item set and episode discovery. The combination of these, called composite episodes, has attracted far less attention in literature, however. The main reason is that the well-known frequent pattern explosion is far worse for composite episodes than it is for item sets or episodes. Yet, there are many applications where composite episodes are required, e.g., in developmental biology were sequences containing gene activity sets over time are analyzed. This paper introduces an effective algorithm for the discovery of a small, descriptive set of composite episodes. It builds on our earlier work employing MDL for finding such sets for item sets and episodes. This combination yields an optimization problem. For the best results the components descriptive power has to be balanced. Again, this problem is solved using MDL.