Mining fuzzy association rules is the task of finding the fuzzy itemsets which frequently occur together in large fuzzy dataset, but most proposed methods may identify a fuzzy rule with two fuzzy itemsets as interesting when, in fact, the presence of one fuzzy itemsets in a record does not imply the presence of the other one in the same record. To prevent generating this kind of misleading fuzz...

Classification based on association rules is considered to be effective and advantageous in many cases. However, there is a so-called "sharp boundary" problem in association rules mining with quantitative attribute domains. This paper aims at proposing an associative classification approach, namely Classification with Fuzzy Association Rules (CFAR), where fuzzy logic is used in partitioning the...

Association rule mining (ARM) aims at extraction, hidden relation, and interesting associations between the existing items in a transactional database. The purpose of this study is to highlight fundamental of association rule mining, association rule mining approaches to mining association rule, various algorithm and comparison between algorithms. Keywords—Assocation Rule Mining; Bottom up Appr...

Negative and positive association rule mining is extract needful information for large database. The generation of negative and positive rule based on interesting pattern and noninteresting pattern of database. The violation of given threshold value such as minimum support and minimum confidence generate some negative rules. The generation of association rule mining dependent some algorithm suc...

ABSTARCT Earlier the uninteresting rules can be shortened through the fuzzy weighted association rule mining with enhanced HITS algorithm that satisfies downward closure property as a consequence of assigning weights to items manually, which can reduce the execution time. In this FWARM there are two main issues of weight calculation, the foremost one is that the algorithm may not find out custo...

Conventional Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. Until recently, rarity has not received much attention in the context of data mining. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. Most Association Rule...

A fuzzy association rule mining mechanism (CFARM), directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using “properties” associated with these composite attributes. The ex...

This work extends fuzzy inference-grams (fingrams) to fuzzy association rules (FAR), yielding FARFingrams. Their analysis pays attention to interpretability issues. An important open problem in association rule mining is the huge number of frequent itemsets and interesting rules to uncover and communicate to the user. FAR-Fingrams address such problem through visual analysis. They ease the sele...

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...

Fuzzy rule based classification systems is one of the most popular in pattern classification problems. The rules in the fuzzy models can be weighted to show the importance of generated rules where all attributes in the antecedent part of the rules have been usually weighted equally. Whereas the contributed attributes in a fuzzy model may have different influences on the decision making, a new m...