Name
Title | ||
|---|---|---|
Silhouette Analysis For Performance Evaluation In Machine Learning With Applications To Clustering |
Abstract | ||
|---|---|---|
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-means based clustering methods have been broadly used and several extensions have been developed to improve the original k-means clustering method such as k-means ++ and kernel k-means. K-means is a linear clustering method; that is, it divides the objects into linearly separable groups, while kernel k-means is a non-linear technique. Kernel k-means projects the elements to a higher dimensional feature space using a kernel function, and then groups them. Different kernel functions may not perform similarly in clustering of a data set and, in turn, choosing the right kernel for an application could be challenging. In our previous work, we introduced a weighted majority voting method for clustering based on normalized mutual information (NMI). NMI is a supervised method where the true labels for a training set are required to calculate NMI. In this study, we extend our previous work of aggregating the clustering results to develop an unsupervised weighting function where a training set is not available. The proposed weighting function here is based on Silhouette index, as an unsupervised criterion. As a result, a training set is not required to calculate Silhouette index. This makes our new method more sensible in terms of clustering concept. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/e23060759 | ENTROPY |
Keywords | DocType | Volume |
k-means, kernel k-means, machine learning, nonlinear clustering, silhouette index, weighted clustering | Journal | 23 |
Issue | ISSN | Citations |
6 | 1099-4300 | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meshal Shutaywi | 1 | 3 | 3.11 |
| Nezamoddin N Kachouie | 2 | 1 | 0.35 |