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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Basak, J. |
| Copyright Year | 2008 |
| Description | Author affiliation: IBM India Res. Lab., New Delhi, India (Basak, J.) |
| Abstract | We present an online adaptive clustering algorithm in a decision tree framework which has an adaptive tree and a code formation layer. The code formation layer stores the representative codes of the clusters and the tree adapts the separating hyperplanes between the clusters. The membership of a sample in a cluster is decided by the tree and the tree parameters are guided by stored codes. The model provides a hierarchical representation of the clusters by minimizing a global objective function as opposed to the existing hierarchical clusterings where a local objective function at every level is optimized. We show the results on real-life data. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 653246 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424421749 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2008.4761261 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Decision trees Clustering algorithms Topology Iterative algorithms Programmable control Adaptive control Binary trees Pattern clustering Stochastic processes Annealing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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