A new nonparametric interpoint distance-based measure for assessment of clustering

K Modes Clustering Algorithm Based on a New Distance Measure

T he leading par tit ional clustering technique, K Modes, is one of the most computationally eff icient clustering methods fo r categ orical data. In the t raditional K Modes algo rithm, the simple matching dissim ilarity measure is used to compute the distance betw een two values of the same catego rical at t ributes. T his compares tw o categorical v alues directly and results in either a dif...

Ontology-based Distance Measure for Text Clustering

Recent work has shown that ontologies are useful to improve the performance of text clustering. In this paper, we present a new clustering scheme on the basis of ontologies-based distance measure. Before implementing clustering process, term mutual information matrix is calculated with the aid of Wordnet and some methods of learning ontologies from textual data. Combining this mutual informatio...

A new Mahalanobis distance measure for clustering of fiber tracts

INTRODUCTION Data analysis in Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is highly sophisticated and can be thought of as a “pipeline” of closely connected processing and modeling steps. Cluster analysis of the orientation of the fiber direction and fiber tracts is typically carried on the major eigenvector. This type of cluster analysis is also important in reducing sorting bias in t...

Some properties of a new fuzzy distance measure

A New Nonparametric Regression for Longitudinal Data

In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus ...