In data mining frequent item-sets mining is one of the important tasks. Apriori is used to mine the frequent item-sets but, Apriori also has some problem as in Apriori finding of support count is very time consuming procedure. To overcome this problem of Apriori, BitApriori algorithm is proposed for mining frequent item-sets, but the BitApriori also suffer the problem of memory scarcity when th...

Frequent item sets mining plays an important role in association rules mining. Over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Therefore, a number of methods have been proposed recently to discover approximate frequent item sets. This paper proposes an efficient SMine (Sorted Mine) Algorithm for finding frequent ite...

The experiments are pursued on both synthetic in data sets are real. The synthetic data sets which we used for our experiments were generated using the procedure. We refer to readers to it for more details to the generation of large data sets. We report experimental results on two synthetic more data sets in this data set; the average transaction of size and its average maximal potentially freq...

This paper presents SaM, a split and merge algorithm for frequent item set mining. Its distinguishing qualities are an exceptionally simple algorithm and data structure, which not only render it easy to implement, but also convenient to execute on external storage. Furthermore, it can easily be extended to allow for “fuzzy” frequent item set mining in the sense that missing items can be inserte...