A Fuzzy Mining Algorithm for Association-Rule Knowledge Discovery" (2005). ABSTRACT ABSTRASCT Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. On the other hand, using fuzzy classification in data mining has been developed in recent years. Hong and Lee proposed a general learning method that ...

This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association can provide guidance assessing significant relationships that be determined while analyzing The approach uses cardinality sets a minimum and maximum range support confidence metrics. A new notation is used enable representation running example queries about desirable features vacat...

This work focuses on the formalization of a Fuzzy Cognitive Map based decision support system using fuzzy If-Then rules (extracted from data) accompanied with the available experts’ knowledge. The proposed approach is applied to build a Fuzzy Cognitive Map (FCM) grading tool, an advanced FCM-based model used for prediction. The FCM is a modeling methodology based on exploiting knowledge and exp...

A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evoluti...

Association rule mining is an important topic in data mining research. Many algorithms have been developed for such task and they typically assume that the underlying associations hidden in the data are stable over time. However, in real world domains, it is possible that the data characteristics and hence the associations change significantly over time. Existing data mining algorithms have not...