EFFICIENT TECHNIQUE FOR MINING FREQUENT PATTERNS OVER DATA STREAM
NIYATI M. MEVADA, JAYNA B. SHAH
frequent itemsets , data stream
Mining frequent items is one of the most important research topics in data mining. In existing system an effective bit-sequence based, one-pass algorithm, called MFI-Trans-SW (Mining Frequent Itemsets within a Transaction Sliding Window), to mine the set of frequent itemsets from data streams within a transaction sliding window which consists of a fixed number of transactions. MFI-TransSW algorithm consists of three phases: window initialization, window sliding and pattern generation. The existing system mines the frequent patterns for the recent data only . In proposed system, we are going to mine the frequent patterns for overall all data. Even the historical data is useful when frequent patterns are mined. As soon as the transaction arrives , each incoming transaction is scanned . If the itemset exist in the transaction the support count is incremented by 1. Otherwise the support count would remain same as it was. Frequent as well as infrequent patterns are maintained in the system. The proposed system not only attain highly accurate mining results, but also run significant faster than existing algorithms for mining frequent itemsets from data streams without using a sliding window.