Towards a New Approach for Mining Frequent Itemsets on Data Stream

However, performance degrades when the length of the maximal frequent itemset is large, since in the bottom-up approach, the maximal frequent itemset is obtained

In [21], a method for finding recently frequent item sets over a data stream based on Mining Maximum Frequent Item Sets over Data Streams Using Transaction Sliding Window

First we apply Apriori Algorithm for frequent itemset generation and that is also generate positive rules, after on frequent itemset we apply NRGA algorithm for all negative

Muraoka, “Data access pattern analysis on stream mining algorithms for cloud computation,” in Proceedings of the International Conference on Parallel and Distributed

In this paper we propose a new approach based on Particle Swarm Optimization Algorithm to mine association rules for both frequent and infrequent itemsets in an efficient way.

In this paper we propose to study the problem of cyclic nature of a list of time intervals associated with a frequent itemset and devise a method to extract all frequent

To evaluate the efficiency of our feature extraction system we compare our frequent pattern mining algorithm results with Apriori that has been used by [10].. We

Clearly, an anonymized dataset (produced by either NG RAMS or by our algorithms) allows accurate mining, when: (i) a high percentage of its frequent sequential patterns are frequent