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DESCRIPTION:
A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams.
The proposed algorithm DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets) is composed of four steps. First, it reads a block of transactions from the buffer in main memory, and sorts the items of transactions in the lexicographical order. Second, it constructs and maintains the in-memory summary data structure SFI-forest (stands for Summary Frequent Itemset forest). Third, it prunes the infrequent patterns from the summary data structure. Fourth, it searches the set of all maximal frequent itemsets from the current summary data structure
A new summary data structure called summary frequent itemset forest (abbreviated as SFIforest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.
SYSTEM REQUIREMENTS
Hardware Requirements :
• Pentium IV 500 MHz
• 512 MB RAM
• 500 MB Free Hard disk space
• Color Monitor
Software Requirements:
Front End : Java
Synthetic datasets
Operating System : Windows XP
RAM : 512 MB
Tools : Edit plus, Microsoft FrontPage for editing