Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient v...

and Applied Analysis 3 x ν αx, t ν x, t/|α| for each α/ 0, xi ν x, t ν y, s ≥ ν x y, t s , xii ν x, · : 0,∞ → 0, 1 is continuous, xiii limt→∞ν x, t 0 and limt→ 0ν x, t 1. In this case μ, ν is called an intuitionistic fuzzy norm. Example 1.4 cf. 37 . Let X, ‖·‖ be a normed space, a∗b ab, and a b min a b, 1 for all a, b ∈ 0, 1 . For all x ∈ X and every t > 0 and k 1, 2, consider μk x, t ⎧ ⎨ ⎩ t t...

Data Mining has great scope in the field of medicine. In this article we introduced one new fuzzy approach for prediction of hepatitis disease. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for reprocessing and then using KNN for classification. In our approach Naive Bayes c...

Klaus S hmid and Volker Krebs Universität Karlsruhe (TH), Institut für Regelungsund Steuerungssysteme Kaiserstr. 12, D-76131 Karlsruhe, Germany e-mail: {s hmid, krebs} irs.ete .uni-karlsruhe.de Abstra t. A dynami fuzzy system is a mapping of fuzzy input values onto a fuzzy output value with a feedba k to the input. In this paper, we present a new rule-based inferen e method that an be used in d...

Constrained k nearest neighbor query for uncertain object in the network is to find k uncertain objects which are the k nearest neighbors with range constraint of the query object in the network. For solving this problem, the uncertain object is modeled as the fuzzy object and the network  -distance between fuzzy objects in the network is defined. Base on them, the concept of constrained k nea...

In this paper, we present a fuzzy k-means clustering algorithm using the cluster center displacement between successive iterative processes to reduce the computational complexity of conventional fuzzy k-means clustering algorithm. The proposed method, referred to as CDFKM, first classifies cluster centers into active and stable groups. Our method skips the distance calculations for stable clust...

In this paper we present an approach to designing a novel type of fuzzy controller B spline basis functions are used for input variables and fuzzy singletons for output variables to specify linguistic terms Product is chosen as the fuzzy conjunction and centroid as the defuzzi ca tion method By appropriately designing the rule base a fuzzy controller can be interpreted as a B spline interpolato...