Improved Data Mining Approach to Find Frequent Itemset Using Support Count Table

Provided by: International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Topic: Data Management
Format: PDF
Mining frequent item sets has been widely studied over the last decade. Past paper focuses on mining frequent itemsets from static database. In many of the new applications mining time series and data stream is an important task now. Last decade, there are mainly two kinds of algorithms on frequent pattern mining. One is apriori based on generating and testing, the other is FP-growth based on dividing and conquering, which has been widely used in static data mining. But with the new requirements of data mining, mining frequent pattern is not restricted in the same scenario.

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