As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Data clustering technique can be used in many fields, such as data mining, statistical data analysis, image analysis, pattern recognition, etc. Good clustering can result in computational reduction in related application programs; however, it is hard to achieve without knowing how many clusters that a data set should be partitioned, which is common in many applications. The way to find the optimal number of clusters is called cluster validity. In this paper, we proposed a new cluster validity indexing method that aims to solve cluster overlapping problem. Our method adapts the concept of cluster validity index defined as the ratio of compactness and separation and enhances it by integrating an entropy-based weight to the definition of separation so that the new weighted-separation of two overlapped clusters will be larger than that of two non-overlapped clusters, where the distance between the two cluster-centroids are the same. Experiments on six synthetic datasets comprising 3 to 10 clusters with some clusters overlapped each other demonstrate that our proposed method achieves 100% accuracy of validity index for all these datasets and is superior to all other compared methods.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.