Contents
What are the applications for association rule?
Applications of association rule mining are stock analysis, web log mining, medical diagnosis, customer market analysis bioinformatics etc. In past, many algorithms were developed by researchers for Boolean and Fuzzy association rule mining such as Apriori, FP-tree, Fuzzy FP-tree etc.
What is frequent itemset in association rule mining?
Frequent itemsets are the ones which occur at least a minimum number of times in the transactions. Technically, these are the itemsets for which support value (fraction of transactions containing the itemset) is above a minimum threshold — minsup.
What are the frequent Itemsets?
Frequent itemsets (Agrawal et al., 1993, 1996) are a form of frequent pattern. Given examples that are sets of items and a minimum frequency, any set of items that occurs at least in the minimum number of examples is a frequent itemset. In such more general settings, the term frequent pattern is often used.
What are the association rules for frequent itemsets?
· The occurrence frequency of an itemset is the number of transactions that contain the itemset. · This is also known, simply, as the frequency, support count, or count of the itemset. Rules that satisfy both a minimum support threshold (min sup) and a minimum confidence threshold (min conf) are called Strong Association Rules.
Association rule mining: Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories. · A set of items is referred to as an itemset. · An itemset that contains k items is a k-itemset.
How to search for frequent items in a data set?
Frequent Item set in Data set (Association Rule Mining) Association Mining searches for frequent items in the data-set. In frequent mining usually the interesting associations and correlations between item sets in transactional and relational databases are found.
When does an itemet become a maximal itemet?
If X is A union B then it is the number of transactions in which A and B both are present. Maximal Itemset: An itemset is maximal frequent if none of its supersets are frequent. Closed Itemset: An itemset is closed if none of its immediate supersets have same support count same as Itemset.