Paper Info
Title
Online Adaptive Clustering In A Decision Tree Framework
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 exisitng hierarchical clusterings where a local objective function at every level is optimized. We show the results on real-life data.
Year
DOI
Venue
2008
10.1109/ICPR.2008.4761261
19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6
Keywords
Field
DocType
decision tree,iris,accuracy,clustering algorithms,objective function,decision trees,hierarchical clustering
Data mining,Tree traversal,Pattern recognition,Computer science,Tree (data structure),Vantage-point tree,Artificial intelligence,ID3 algorithm,Segment tree,Fractal tree index,Interval tree,Incremental decision tree
Conference
ISSN
Citations 
PageRank 
1051-4651
1
0.35
References 
Authors
7
1
Name
Order
Citations
PageRank
Jayanta Basak137232.68