Title :
Online adaptive clustering in a decision tree framework
Author_Institution :
IBM India Res. Lab., New Delhi, India
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.
Keywords :
decision trees; pattern clustering; adaptive tree; code formation layer; decision tree framework; online adaptive clustering; Adaptive control; Annealing; Binary trees; Clustering algorithms; Decision trees; Iterative algorithms; Pattern clustering; Programmable control; Stochastic processes; Topology;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761261