Frequent Itemset Mining is an important approach for Market Basket Analysis. Earlier, the frequent itemsets are determined based on the customer transactions of binary data. Recently, fuzzy data are used to determine the frequent itemsets because it provides the nature of frequent itemset ie. , it describes whether the frequent itemset consists of only highly purchased items or medium purchased...

Frequent Itemset Mining (FIM) is one of the most investigated fields of data mining. The goal of Frequent Itemset Mining (FIM) is to find the most frequently-occurring subsets from the transactions within a database. Many methods have been proposed to solve this problem, and the Apriori algorithm is one of the best known methods for frequent Itemset mining (FIM) in a transactional database. In ...

Utility-based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utilitybased descriptive data mining, aimed at finding itemsets that contribute most to the total utility. A specialized fo...

Mining frequent itemset using bit-vector representation approach is very efficient for small dense datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. We also present a new frequent itemset mining algorithm Ramp (Real Algorithm...

Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and ...