Name
Abstract | ||
|---|---|---|
In this paper, we develop a family of data clustering algorithms that combine the strengths of existing spectral approaches to clustering with various desirable properties of fuzzy methods. In particular, we show that the developed method "Fuzzy-RW," outperforms other frequently used algorithms in data sets with different geometries. As applications, we discuss data clustering of biological and face recognition benchmarks such as the IRIS and YALE face data sets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11634-013-0125-7 | Adv. Data Analysis and Classification |
Keywords | Field | DocType |
various desirable property,fuzzy method,spectral approach,face recognition benchmarks,yale face data set,random walk distance,different geometries,developed method,mahalanobis,random walks,graph laplacian,spectral clustering | Data mining,Fuzzy clustering,CURE data clustering algorithm,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Clustering high-dimensional data,Data stream clustering,Pattern recognition,Correlation clustering,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 1 | 1862-5355 |
Citations | PageRank | References |
1 | 0.37 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sijia Liu | 1 | 181 | 42.37 |
| Anastasios Matzavinos | 2 | 2 | 1.36 |
| Sunder Sethuraman | 3 | 1 | 0.71 |