HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach Export

@article{citeulike:5094539, abstract = {In this paper we present a novel hybrid (arraybased layout and vertical bitmap layout) database representation approach for mining complete Maximal Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms of scalability, item search order and two horizontal and vertical projection techniques. We also present a maximal algorithm using this hybrid database representation approach. Different experimental results on real and sparse benchmark datasets show that our approach is better than previous state of art maximal algorithms.}, archivePrefix = {arXiv}, author = {Bashir, Shariq and Baig, Abdul R.}, citeulike-article-id = {5094539}, citeulike-linkout-0 = {http://arxiv.org/abs/0904.3312}, citeulike-linkout-1 = {http://arxiv.org/pdf/0904.3312}, eprint = {0904.3312}, keywords = {sequential\_pattern}, month = {Apr}, posted-at = {2009-07-09 02:44:10}, priority = {2}, title = {HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach}, url = {http://arxiv.org/abs/0904.3312}, year = {2009} }

TY - JOUR ID - citeulike:5094539 L3 - citeulike-article-id:5094539 N2 - In this paper we present a novel hybrid (arraybased layout and vertical bitmap layout) database representation approach for mining complete Maximal Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms of scalability, item search order and two horizontal and vertical projection techniques. We also present a maximal algorithm using this hybrid database representation approach. Different experimental results on real and sparse benchmark datasets show that our approach is better than previous state of art maximal algorithms. TI - HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach KW - sequential_pattern AU - Bashir, Shariq AU - Baig, Abdul PY - 2009/Apr/21/ UR - http://arxiv.org/abs/0904.3312 ER -