Name
Abstract | ||
|---|---|---|
In high dimensional data, the general performance of tradi- tional clustering algorithms decreases. This is partly bec ause the similarity criterion used by these algorithms becomes inadequate in high dimensional space. Another reason is that some dimensions are likely to be irrelevant or contain noisy data, thus hiding a possible clustering. To overcome these problems, subspace clustering techniques, which can automatically find clusters in relevant subsets of dimensio ns, have been developed. However, due to the huge number of subspaces to consider, these techniques often lack efficien cy. In this paper we propose to extend the framework of bottom- up subspace clustering algorithms by integrating background knowledge and, in particular, instance-level constraints to speed up the enumeration of subspaces. We show how this new framework can be applied to both density and distance- based bottom-up subspace clustering techniques. Our exper- iments on real datasets show that instance-level constrain ts cannot only increase the efficiency of the clustering proces s but also the accuracy of the resultant clustering. |
Year | Venue | Keywords |
|---|---|---|
2009 | SDM | high dimensional data,bottom up |
Field | DocType | Citations |
Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Constrained clustering,Artificial intelligence,Cluster analysis,Machine learning | Conference | 8 |
PageRank | References | Authors |
0.46 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Élisa Fromont | 1 | 192 | 25.51 |
| Adriana Prado | 2 | 87 | 5.14 |
| Céline Robardet | 3 | 696 | 60.36 |