Name
Title | ||
|---|---|---|
The novel seeding-based semi-supervised fuzzy clustering algorithm inspired by diffusion processes |
Abstract | ||
|---|---|---|
Semi-supervised clustering can take advantage of some labeled data called seeds to bring a great benefit to the clustering of unlabeled data. This paper uses the seeding-based semi-supervised idea for a fuzzy clustering method inspired by diffusion processes, which has been presented recently. To investigate the effectiveness of our approach, experiments are done on three UCI real data sets. Experimental results show that the proposed algorithm can improve the clustering performance significantly compared to other semi-supervised clustering approaches. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39065-4_68 | ISNN (1) |
Keywords | Field | DocType |
seeding-based semi-supervised idea,semi-supervised clustering,uci real data set,great benefit,fuzzy clustering algorithm,diffusion process,clustering performance,semi-supervised clustering approach,unlabeled data,fuzzy clustering method,fuzzy clustering,seeding | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,FLAME clustering,Cluster analysis,Canopy clustering algorithm,Clustering high-dimensional data,Correlation clustering,Pattern recognition,Algorithm,Brown clustering,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
1 | ||