A fuzzy association rule algorithm is implemented to extract the relationship between the atmospheric indices and the Indian Summer Monsoon Rainfall (ISMR). ENSO and EQWIN indices are used as the causative variables. Rules extracted are showing a negative relationship with ENSO index and a positive relationship with the EQWIN index. A fuzzy rule based prediction technique is also implemented on...

This paper is focused on studying how data privacy could be preserved with fuzzy rule bases as interpretable as possible. These fuzzy rule bases are obtained from a data mining strategy based on building a decision tree. The antecedents of each rule produced by these systems contain information about the released variables (quasiidentifier) whereas the consequent contains information only about...

In this paper, Fuzzy concept hierarchies are used for multi-level association rule mining from large datasets via Attribute-Oriented Induction approach [1]. In this the process of fuzzy hierarchical induction approach is used and extends it with two new characteristics which improve applicability of the original approach in data mining. The proposed drilling-down approach of fuzzy induction mod...

A number of techniques have been developed to turn data into useful knowledge. Most of the algorithms in data mining find association rules among transactions using binary values and at single concept level. However it will be more exciting to discover hierarchical association rules for decision makers. In this work we have integrated association rule mining with fuzzy set theory and hierarchy....

Construction of effective and accurate classifier is one of the challenges facing by the researchers. Many experiments have shown that Associative Classifier is significantly more accurate than the traditional classifiers. To classify the quantitative data, Fuzzy Associative Classifier was introduced and is also proved as an effective prediction model. Mining of negative association rules have ...

Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However ...

this paper investigates existence and uniqueness results for the first order fuzzy integro-differential equations. then numerical results and error bound based on the left rectangular quadrature rule, trapezoidal rule and a hybrid of them are obtained. finally an example is given to illustrate the performance of the methods.

This paper considers the automatic design of fuzzy rule-based classification systems from labeled data. The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems. We propose a weighting function for compatibility grade of patterns that improves the performance of fuzzy classification system without degrading the interpretability ...

A novel approach is presented for mining weighted association rules (ARs) from binary and fuzzy data. We address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance w.r.t some user defined criteria. Most works on weighted association rule mining so far struggle with invalid downward c...

finding frequent patterns plays a key role in exploring association patterns, correlation, and many other interesting relationships that are applicable in tdb. several association rule mining algorithms such as apriori, fp-growth, and eclat have been proposed in the literature. fp-growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract...