Grouping the objects based on their similarities is an important common task in machine
learning applications. Many clustering methods have been developed, among them k …

Clustering is an important phase in data mining. Selecting the number of clusters in a
clustering algorithm, eg choosing the best value of k in the various k-means algorithms [1] …

This paper analyses the application of Simplified Silhouette to the evaluation of k-means
clustering validity and compares it with the k-means Cost Function and the original …

The problem of estimating the number of clusters (say k) is one of the major challenges for
the partitional clustering. This paper proposes an algorithm named k-SCC to estimate the …

In K-Means based clustering algorithms, different initial seeds can lead to different clustering
results. Selecting the best result from different initial seeds is called the filtering process. The …

The traditional k-means, which unambiguously assigns an object precisely to a single
cluster with crisp boundary, does not adequately show the fact that a cluster may not have a …

Finding compact and well-separated clusters in data sets is a challenging task. Most
clustering algorithms try to minimize certain clustering objective functions. These functions …

Clustering is an important technology that can divide data patterns into meaningful groups,
but the number of groups is difficult to be determined. This paper proposes an automatic …

Abstract The Average Silhouette Width (ASW) is a popular cluster validation index to
estimate the number of clusters. The question whether it also is suitable as a general …

Many similarity-based clustering methods work in two separate steps including similarity
matrix computation and subsequent spectral clustering. However similarity measurement is …