Authors: Dai Caiyan; Chen Ling
Addresses: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, No. 24, YuDao Street, Baixia District, Nanjing, China ' Department of Computer Science, Yangzhou University, No. 198, Huayang Road, Hanjiang District, Yangzhou, China
Abstract: Mining frequent closed itemsets from data streams is an important topic. In this paper, we propose an algorithm for mining frequent closed itemsets from data streams based on a time fading module. By dynamically constructing a pattern tree, the algorithm calculates densities of the itemsets in the pattern tree using a fading factor. The algorithm deletes real infrequent itemsets from the pattern tree so as to reduce the memory cost. A density threshold function is designed in order to identify the real infrequent itemsets which should be deleted. Using such density threshold function, deleting the infrequent itemsets will not affect the result of frequent itemset detecting. The algorithm modifies the pattern tree and detects the frequent closed itemsets in a fixed time interval so as to reduce the computation time. We also analyse the error caused by deleting the infrequent itemsets. The experimental results indicate that our algorithm can get higher accuracy results, and needs less memory and computation time than other algorithm.
Keywords: data streams; frequent closed itemsets; FCI; data mining; time fading models; density threshold.
International Journal of Computational Science and Engineering, 2016 Vol.12 No.2/3, pp.146 - 154
Available online: 28 Apr 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article