Research
OSSM: Ordered Sequence set mining for maximal length frequent sequences
The process of finding sequential rules is an indispensable in frequent sequence mining. Generally, in sequence mining algorithms, suitable methodologies like a bottom–up approach will be used for creating large sequences from tiny patterns. This paper proposed on an algorithm that uses a hybrid two-way (bottom-up and top-down) approach for mining maximal length sequences. The model proposed is opting to bottom-up approach called “Concurrent Edge Prevision and Rear Edge Pruning (CEG&REP)†for itemset mining and top-down approach for maximal length sequence mining. It also explains optimality of top-to-bottom approach in deriving maximal length sequences first and lessens the scanning of the dataset.
Frequent Pattern mining with closeness Considerations: Current State of the art
Due to rising importance in frequent pattern mining in the field of data mining research, tremendous progress has been observed in fields ranging from frequent itemset mining in transaction databases to numerous research frontiers. An elaborative note on current condition in frequent pattern mining and potential research directions is discussed in this article. It’s a strong belief that with considerably increasing research in frequent pattern mining in data analysis, it will provide a strong foundation for data mining methodologies and its applications which might prove a milestone in data mining applications in mere future.
