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...

An approach to determining the optimal cell number of manufacturing cell formation is presented. Firstly, the difference of weighting exponent, cluster center and metrics how to have an impact upon the clustering results and membership function are studied. Secondly, a method to determine the optimal m value is given. Two-order partial derivative of the objective function for FCM is calculated,...

Although the goal of clustering is intuitively compelling and its notion arises in many fields, it has been difficult to define a unified approach to address the clustering problem and thus diverse clustering approaches abound in the research community. These approaches are based on different clustering principles and assumptions and they often lead to qualitatively different results. As a cons...

OBJECTIVES Empirical evidence of discriminative validity is required to justify the use of mechanisms-based classifications of musculoskeletal pain in clinical practice. The purpose of this study was to evaluate the discriminative validity of mechanisms-based classifications of pain by identifying discriminatory clusters of clinical criteria predictive of "nociceptive," "peripheral neuropathic,...

On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability fi...

This paper presents a method for the assessment of expression cluster validity.

Short texts clustering is one of the most difficult tasks in natural language processing due to the low frequencies of the document terms. We are interested in analysing these kind of corpora in order to develop novel techniques that may be used to improve results obtained by classical clustering algorithms. In this paper we are presenting an evaluation of different internal clustering validity...

Abstract Clustering can be defined as the process of grouping physical or abstract objects into classes of similar objects. It’s an unsupervised learning problem of organizing unlabeled objects into natural groups in such a way objects in the same group is more similar than objects in the different groups. Conventional clustering algorithms cannot handle uncertainty that exists in the real life...

Cluster validity indices are used for both estimating the quality of a clustering algorithm and for determining the correct number of clusters in data. Even though several indices exist in the literature, most of them are only relevant for data sets that contain at least two clusters. This paper introduces a new bounded index for cluster validity called the score function (SF), a double exponen...

introduction: now a day, resulting from life style modification and alteration, fatigue, as a consequence, is not uncommon symptom. diverse tools are available to measure fatigue status. one of which is facit fatigue scale. the facit is one of the most widely used questionnaires for screening fatigue. the questionnaire has been translated and validated? into 45 different languages, but there is...