Name
Abstract | ||
|---|---|---|
We present a novel soft clustering approach based on Support Vector Clustering.Data points outside the clusters found form a fuzzy boundary region.Clusters with any shape as well as outliers can be identified.Membership degrees were calculated in a natural way. Support Vector Clustering (SVC) is an important density-based clustering algorithm which can be applied in many real world applications given its ability to handle arbitrary cluster silhouettes and detect the number of classes without any prior knowledge. However, if outliers are present in the data, the algorithm leaves them unclassified, assigning a zero membership degree which leads to all these objects being treated in the same way, thus losing important information about the data set. In order to overcome these limitations, we present a novel extension of this clustering algorithm, called Rough-Fuzzy Support Vector Clustering (RFSVC), that obtains rough-fuzzy clusters using the support vectors as cluster representatives. The cluster structure is characterized by two main components: a lower approximation, and a fuzzy boundary. The membership degrees of the elements in the fuzzy boundary are calculated based on their closeness to the support vectors that represent a specific cluster, while the lower approximation is built by the data points which lie inside the hyper-sphere obtained in the training phase of the SVC algorithm. Our computational experiments verify the strength of the proposed approach compared to alternative soft clustering techniques, showing its potential for detecting outliers and computing membership degrees for clusters with any silhouette. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ins.2015.12.035 | Inf. Sci. |
Keywords | Field | DocType |
Fuzzy sets,Rough sets,Support Vector Clustering,Data mining | Fuzzy clustering,CURE data clustering algorithm,Correlation clustering,Pattern recognition,Determining the number of clusters in a data set,Artificial intelligence,Constrained clustering,FLAME clustering,Cluster analysis,Machine learning,Mathematics,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
339 | C | 0020-0255 |
Citations | PageRank | References |
13 | 0.48 | 28 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ramiro saltos | 1 | 13 | 0.48 |
| R. Weber | 2 | 85 | 7.55 |