Understanding Data through Clustering

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

Environmental data inference through fuzzy clustering

Retrieving the relevant information from noisy environmental data is a difficult task, which requires advanced filtering techniques. This paper presents a method for extracting representative prototypes from a set of data containing daily and seasonal fluctuations. It is based on a combination of wavelet filtering and fuzzy clustering. Discriminating features are extracted with a Fuzzy Maximum ...

Analysing microarray expression data through effective clustering

Data Clustering And Visualization Through Matrix Factorization

ACKNOWLEDGMENTS First of all, I would like to express my special appreciation to my advisor, Dr. Ming Dong, for his guide of my professional development and an inexhaustible source of ideas through my Ph.D.program at Wayne State University. During these years, he has spent tremendous time and effort with me discussing research, teaching me to write papers, and answering my questions. Without hi...