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Information granules emerging as a result of an abstract and more condensed and global view at numeric data play an essential role in various pattern recognition pursuits. In this study, we investigate an idea of granular prototypes (representatives) and discuss their role in the realization of classification schemes. A two-stage procedure of a formation of information granules is discussed. We show...
Feature selection plays an important part in improving the classification accuracy and the quality of clustering in many applications. Feature selection has been widely studied in supervised learning, but in unsupervised learning it is still relatively rare. In this paper, a novel definition of feature differentiation for identifying (determining) the relatively important features is presented, and...
Nowadays, clustering algorithms are widely used in the commercial field, such as customer analysis, and this application has achieved good effect. K-means algorithm is by far the most commonly used method for clustering. Although, the time consumption is fairly high when faced with lager-scale data. In this paper, we improved the K-means algorithm. Our improvement is based on the triangle inequality...
The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most...
In this paper we have developed a connectivity based cluster validity index. This validity index is able to detect the number of clusters automatically from data sets having well separated clusters of any shape, size or convexity. The proposed cluster validity index, connect-index, uses the concept of relative neighborhood graph for measuring the amount of "connectedness" of a particular...
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