Data mining is most commonly used in attempts to induce association rules from transaction
data. In the past, we used the fuzzy and GA concepts to discover both useful fuzzy …

The health care environment still needs knowledge based discovery for handling wealth of
data. Extraction of the potential causes of the diseases is the most important factor for …

Classification systems such as rock mass rating (RMR) are used to evaluate rock mass
quality. This paper intended to evaluate RMR based on a fuzzy clustering algorithm to …

Healthcare data need an accurate diagnosis of diseases with the low computation time.
Fuzzy association rule mining converts quantitative attributes to fuzzy attributes which …

Data mining is very popular recently due to lots of analysis applications of big data. A well-
known algorithm for mining association rules from transactions is the Apriori algorithm …

Frequent pattern mining is one of the most important research topics for many real life
applications in the area of data mining. Frequent item set originates from association rule …

Fuzzy K-means clustering algorithm is very much useful for exploring the structure of a set of
patterns, especially when the clusters are overlap**. K-means algorithm is simple with low …

This study adapted an improved algorithm based on Artifical Bee Colony Optimization. It is
not possible to justify that all the rules generated by fuzzy based apriori algorithm produce …

Quantitative multilevel association rules mining is a central field to realize motivating
associations among data components with multiple levels abstractions. The problem of …

This paper proposed a prediction model based on Fuzzy Association Rule (FARs). Present a
two model; it is the integration of the Fuzzy C-Means (FCM) and Multiple Support Apriori (MS …