Discovering Frequent Sets from Data Streams with CPU Constraint
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recognized that mining over streaming data requires the problem of limited computational resources to be adequately addressed. Since the arrival rate of data streams can significantly increase and exceed the CPU capacity, the machinery must adapt to this change to guarantee the timeliness of the results. The authors present an online algorithm to approximate a set of frequent patterns from a sliding window over the underlying data stream - given apriori CPU capacity.