This paper describes how to discover dynamic relationships among genes from time series microarray data with association rule mining approach. To hold dynamic information in the rules, the association rules were extracted using the constraints that expression level of genes appear in the antecedent and change direction of expression level of genes in the consequent. Besides, we have applied fuz...

Data mining methods including association rule mining and frequent episode mining have been applied to the intrusion detection problem. In other work, we have introduced modifications of these methods that mine fuzzy association rules and fuzzy frequent episodes and have described off-line methods that utilize these fuzzy methods for anomaly detection from audit data. In this paper we describe ...

Modern medicine generates a great deal of information stored in the medical database. Extracting useful knowledge and providing scientific decision making for the diagnosis and treatment of disease from the database increasingly becomes necessary. In India most of the people suffering cancer diseases. Using association rule mining for constructing classification system for diagnosing cancer dis...

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact ...

Association rule learning is a data mining task that tries to discover interesting relations between variables in large databases. A review of association rule learning is presented that focuses on the use of evolutionary algorithms not only applied to Boolean variables but also to categorical and quantitative ones. The use of fuzzy rules in the evolutionary algorithms for association rule lear...

The problem of mining association rules in a database are introduced. Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. A new algorithm called ―Without expert fuzzy based data mining Based on Fuzzy Similarity to mine new Association Rules ‖ which considers not only exact matches between items, but also the fuzzy sim...

Conventional Association Rule Mining (ARM) algorithms usually deal with datasets with categorical values and expect any numerical values to be converted to categorical ones using ranges (Age = 25 to 60). Fuzzy logic is used to convert quantitative values of attributes to categorical ones so as to eliminate any loss of information arising due to sharp partitioning (using ranges) and then generat...