Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm
Sanjeev Rao, Priyanka Gupta · 2012
In this paper we present new scheme for extracting association rules that considers the time, number of database scans, memory consumption, and the interestingness of the rules. Discover a FIS data mining association algorithm that removes the disadvantages of APRIORI algorithm and is efficient in terms of number of database scan and time. The frequent patterns algorithm without candidate generation eliminates the costly candidate generation. It also avoids scanning the database again and again. So, we use Frequent Pattern (FP) Growth ARM algorithm that is more efficient structure to mine patterns when database grows.