Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
MFISW: a new method for mining frequent itemsets in time and transaction sensitive sliding window
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Mining informative rule set for prediction over a sliding window
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
A false negative maximal frequent itemset mining algorithm over stream
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Computers & Mathematics with Applications
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
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In general, the number of frequent itemsets in a data set is very large. In order to represent them in more compact notation, closed or maximal frequent itemsets (MFIs) are used. However, the characteristics of a data stream make such a task be more difficult. For this purpose, this paper proposes a method called estMax that can trace the set of MFIs over a data stream. The proposed method maintains the set of frequent itemsets by a prefix tree and extracts all of MFIs without any additional superset/subset checking mechanism. Upon processing a newly generated transaction, its longest matched frequent itemsets are marked in a prefix tree as candidates for MFIs. At the same time, if any subset of these newly marked itemsets has been already marked as a candidate MFI, it is cleared as well. By employing this additional step, it is possible to extract the set of MFIs at any moment. The performance of the proposed method is comparatively analyzed by a series of experiments to identify its various characteristics.