Name
Abstract | ||
|---|---|---|
The problem this paper focuses on is the classical problem of unsupervised clustering of a data-set. In particular, the bisecting divisive clustering approach is here considered. This approach consists in recursively splitting a cluster into two sub-clusters, starting from the main data-set. This is one of the more basic and common problems in fields like pattern analysis, data mining, document retrieval, image segmentation, decision making, etc. ([13], [15]). Note that by recursively using a bisecting divisive clustering procedure, the data-set can be partitioned into any given number of clusters. Interestingly enough, the so-obtained clusters are structured as a hierarchical binary tree (or a binary taxonomy). This is the reason why the bisecting divisive approach is very attractive in many applications (e. g. in document-retrieval/indexing problems-see e. g. [23]). Any divisive clustering algorithm can be divided into two sub-problems: |
Year | Venue | Keywords |
|---|---|---|
2002 | SIAM Proceedings Series | image segmentation,document retrieval,pattern analysis,binary tree,data mining,indexation |
Field | DocType | Citations |
Fuzzy clustering,Data mining,Document clustering,Computer science,Artificial intelligence,Cluster analysis,Single-linkage clustering,Hierarchical clustering,k-medians clustering,Correlation clustering,Pattern recognition,Hierarchical clustering of networks,Machine learning | Conference | 22 |
PageRank | References | Authors |
1.36 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio M. Savaresi | 1 | 943 | 142.05 |
| Daniel Boley | 2 | 116 | 22.51 |
| Sergio Bittanti | 3 | 219 | 74.16 |
| Giovanna Gazzaniga | 4 | 22 | 2.04 |