Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Maximally informative k-itemsets and their efficient discovery
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Finding low-entropy sets and trees from binary data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Mining non-redundant high order correlations in binary data
Proceedings of the VLDB Endowment
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How to select interesting feature sets from data streams is a new and important research topic in which there are three major challenges. First of all, instead of discovering features individually and independently, we are interested in comprehensively selecting a subset of features whose joint importance or weight is the highest. Secondly, we are concerned with the problem of selecting feature sets over dynamic, large and online data streams which are only partly available when we are selecting the features. This problem distinguishes itself over the data streams from the ones on the static data which is completely available before the feature selection. Finally, data streams may evolve over time, requiring an online feature selection technique which can capture and adapt to such changes. We introduce the problem of online feature selection over data streams and we provide a heuristic solution. We also demonstrate the effectiveness and efficiency of our method through experiments on real-world mobile web usage data.