In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely used in the development of probability models in machine learning in recent yea...

We analyze algorithms that, under the right circumstances, permit efficient mining for frequent itemsets in data with tall peaks (large frequent itemsets). We develop a family of level-by-level peak-jumping algorithms, and study them using a simple probability model. The analysis clarifies why the jumping idea sometimes works well, and which properties the data needs to have for this to be the ...

During the past decade, stream data mining has been attracting widespread attentions of the experts and the researchers all over the world and a large number of interesting research results have been achieved. Among them, frequent itemset mining is one of main research branches of stream data mining with a fundamental and significant position. In order to further advance and develop the researc...

Cloud computing uses the paradigm of data mining-as-a-service. A company/store lacking in mining expertise can outsource its mining needs to a service provider (server). The item-set of the outsourced database are the private property of the data owner. To protect this corporate privacy, the data owner encrypts the data and sends to the server. Based on the mining queries sent from client side,...

Many researchers invented ideas to generate the frequent itemsets. The time required for generating frequent itemsets plays an important role. Some algorithms are designed, considering only the time factor. Our study includes depth analysis of algorithms and discusses some problems of generating frequent itemsets from the algorithm. We have explored the unifying feature among the internal worki...

A new scheme to parallelize frequent itemset mining algorithms is proposed. By using the extended conditional databases and k-prefix search space partitioning, our new scheme can create more parallel tasks with better balanced execution times. An implementation of the new scheme with FP-trees is presented. The results of the experimental evaluation showing the increased speedup are presented.

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The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LS...