UML distilled (2nd ed.): a brief guide to the standard object modeling language
UML distilled (2nd ed.): a brief guide to the standard object modeling language
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Uml Weekend Crash Course
Professional UML with Visual Studio .NET: Unmasking Visio for Enterprise Architects
Professional UML with Visual Studio .NET: Unmasking Visio for Enterprise Architects
Introduction to Clustering Large and High-Dimensional Data
Introduction to Clustering Large and High-Dimensional Data
Systems Engineering with SysML/UML: Modeling, Analysis, Design
Systems Engineering with SysML/UML: Modeling, Analysis, Design
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Recently, huge amount of data have been collected over the past three decades or so. The availability of such data and the imminent need for transforming such data is the functionality of the field of Knowledge Discovery in Database (KDD). The most essential step in KDD is the Data Mining (DM) step which the engine of finding the implicit knowledge from the data. DM tasks can be classified into two types, namely; predictive and descriptive according to the sought functionality. One of the old and well-studied concepts in data mining is cluster analysis. In general, clustering methods can be either hierarchal or partitioning. One of the very well known clustering methods is the C-means. In this paper, the focus is on cluster analysis in general and on the partitioning method C-means in particular. Our attention is on the modification of the C-means algorithm in the way of calculating the means of the clusters. We considered the mean of a cluster to be an object instead of being an imaginary point in the cluster. Our modified Cmeans algorithm is implemented in a system developed in visual basic.net programming language. A number of data sets in the form of experiments tests our system. Our study will concluded with analysis and discussion of the experiments' result on the bases of several criteria.