In this paper, a cluster validity measure is presented to infer the appropriateness of data partitions. The proposed validity measure adopts a novel non-metric distance measure based on the idea of "point symmetry". The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. The performance evaluation of the validity measure compares favor...

In this paper we propose a measure of sim­ ilarity /association between two partitions of a set of objects. Our motivation is the desire to use the measure to characterize the quality or accuracy of clustering algorithms by some­ how comparing the clusters they produce with "ground truth" consisting of classes as­ signed by manual means or some other means in whose veracity there is confidence....

To measure the fuzziness of fuzzy sets, this paper introduces a distance-based and a fuzzy entropybased measurements. Then these measurements are generalized to measure the fuzziness of fuzzy partition, namely partition fuzziness. According to the relationship between the validity of fuzzy partition and its partition fuzziness, a family of cluster validity functions is proposed based on the mod...

Data Clustering is a technique of finding similar characteristics among the data set which are always hidden in nature and grouping them into groups, called as clusters. Different clustering algorithms exhibit different results, since they are very sensitive to the characteristics of original data set especially noise and dimension. The quality of such clustering process determines the purity o...

Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure, which can deal with this situation. The performance evaluation of the validity measure comp...

The k-means method has been shown to be effective in producing good clustering results for many practical applications. However, a direct algorithm of k-means method requires time proportional to the product of number of patterns and number of clusters per iteration. This is computationally very expensive especially for large datasets. The main disadvantage of the k-means algorithm is that the ...

Many real-world and man-made objects are symmetry, therefore, it is reasonable to assume that some kind of symmetry may exist in data clusters. In this paper a new cluster validity measure which adopts a non-metric distance measure based on the idea of “line symmetry” is presented. The proposed validity measure can be applied in finding the number of clusters of different geometrical structures...

A heuristic approach to possibilistic clustering is the effective tool for the data analysis. The approach is based on the concept of allotment among fuzzy clusters. To establish the number of clusters in a data set, a validity measure is proposed in this paper. An illustrative example of application of the proposed validity measure to the Anderson’s Iris data is given. A comparison of the vali...