Name
Title | ||
|---|---|---|
Applying a novel decision rule to the semi-supervised clustering method based on one-class SVM |
Abstract | ||
|---|---|---|
Semi-supervised clustering takes advantage of some labeled data called seeds to bring a great benefit to the clustering of unlabeled data. This paper presents a novel semi-supervised clustering method based on one-class support vector machine, which applies a novel decision rule to assigning the class label to one data point. To investigate the effectiveness of our approach, experiments are done on one artificial data set and two real datasets. Experimental results show that the proposed method can improve the clustering performance significantly compared to other semi-supervised clustering algorithms when using a very small amount of seeds. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31346-2_15 | ISNN (1) |
Keywords | Field | DocType |
semi-supervised clustering,one-class svm,novel decision rule,artificial data,data point,clustering performance,clustering method,class label,semi-supervised clustering method,unlabeled data,semi-supervised clustering algorithm,decision rule | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Pattern recognition,Determining the number of clusters in a data set,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
1 | ||