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Most of the existing algorithms for mining frequent items on data stream do not emphasis the importance of the recent data items. We present an algorithm using a fading factor to detect the data items with frequency counts exceeding a user-specified threshold. Our algorithm can detect ??-approximate frequent data items on data stream using O(??-1) memory space and the processing time for each data...
An algorithm using a fading factor to detect the frequent data items in a stream is presented. Our algorithm can detect epsiv-approximate frequent data items on data stream using O(L+epsiv-1) memory space where L is a constant, and the processing time for each data item is O(1). Experimental results on several artificial datasets and real datasets show our algorithm has higher precision, requires...
Most of the existing algorithms for mining frequent items over data streams do not emphasis the importance of the more recent data items. We present an efficient algorithm where a fading factor lambda is used for computing frequency counts exceeding a user-specified threshold over data streams. Our algorithm lambda-Miner can detect epsilon-approximate frequent items of a data stream using O(epsilon−1)...
Efficient algorithms with time fading model for mining frequent items over data stream are presented. Our algorithm FC2 can detectepsiv-approximate frequent items of a data stream using O(epsiv-1) memory space and the processing time for each data item is O(1). Experimental results on several artificial data sets and real data sets show that our methods have high precision, require less memory and...
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