Name
Abstract | ||
|---|---|---|
Alternative c-means is an extension of k-means-type clustering for robustifying cluster estimation, in which a modified distance measure instead of the conventional Euclidean distance is used based on the robust M-estimation concept. In this paper, alternative c-means is further extended to linear clustering models with line-shape prototypes, in which the clustering criteria of distances between data samples and linear prototypes are calculated by the lower rank approximation concept. The iterative updating scheme is derived in a pseudo-M-estimation procedure with a weight function for the modified distance measure and is demonstrated to be useful for extracting linear substructures from noisy datasets. In numerical experiments, the model is applied to POS transaction data analysis based on local PCA-like data summarisation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1504/IJKESDP.2011.045728 | IJKESDP |
Keywords | Field | DocType |
linear prototype,conventional euclidean distance,data sample,linear substructure,local principal component extraction,alternative fuzzy c-lines,clustering criterion,linear clustering model,alternative c-means,k-means-type clustering,modified distance measure,pos transaction data analysis,pca,fuzzy clustering,principal component analysis | k-medians clustering,Hierarchical clustering,Fuzzy clustering,CURE data clustering algorithm,Clustering high-dimensional data,Correlation clustering,Pattern recognition,Artificial intelligence,Cluster analysis,Mathematics,Machine learning,Single-linkage clustering | Journal |
Volume | Issue | Citations |
3 | 2 | 3 |
PageRank | References | Authors |
0.45 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Katsuhiro Honda | 1 | 289 | 63.11 |
| Sakuya Nakao | 2 | 3 | 0.79 |
| Akira Notsu | 3 | 146 | 42.93 |
| Hidetomo Ichihashi | 4 | 370 | 72.85 |