Discovery of frequent itemsets is one of the fundamental data mining problems. Typically, the goal is to discover all the itemsets whose support in the source dataset exceeds a user-specified threshold. However, very often users want to restrict the set of frequent itemsets to be discovered by adding extra constraints on size and contents of the itemsets. Many constraint-based frequent itemset ...

Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm. We wil...

Over the last decades, frequent itemset mining has become a major area of research, with applications including indexing and similarity search, as well as mining of data streams, web, and software bugs. Although several efficient techniques for generating frequent itemsets with a minimum support (frequency) have been proposed, the number of itemsets produced is in many cases too large for effec...

A fundamental task of data mining is to mine frequent itemsets. Since the number of frequent itemsets may be large, a compact representation, namely the max frequent itemsets, has been introduced. On the other hand, the concept of generalized itemsets was proposed. Here, the items form a taxonomy. Although the transactional database only contains items in the leaf level of the taxonomy, a gener...

In customary, frequent itemsets are propogated from large data sets by employing association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental and Border algorithm etc., which gains inordinately longer computer time to cast up all the frequent itemsets. On utilizing Genetic Algorithm (GA) the scheme is reformed.. The outstanding benefit of utilizing GA in determining th...

There are many algorithms developed for improvement the time of mining frequent itemsets (FI) or frequent closed itemsets (FCI). However, the algorithms which deal with the time of generating association rules were not put in deep research. In reality, in case of a database containing many FI/FCI (from ten thousands up to millions), the time of generating association rules is much larger than t...