Adapting k-means for supervised clustering
| Autor: | S. H. Al-Harbi, Victor J. Rayward-Smith |
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| Rok vydání: | 2006 |
| Předmět: |
DBSCAN
Clustering high-dimensional data Fuzzy clustering Computer science Correlation clustering Single-linkage clustering Conceptual clustering computer.software_genre Artificial Intelligence CURE data clustering algorithm Consensus clustering Cluster analysis k-medians clustering Brown clustering k-medoids business.industry k-means clustering Pattern recognition Data set ComputingMethodologies_PATTERNRECOGNITION Data stream clustering Simulated annealing Canopy clustering algorithm FLAME clustering Affinity propagation Artificial intelligence Data mining Hierarchical clustering of networks business computer |
| Zdroj: | Applied Intelligence. 24:219-226 |
| ISSN: | 1573-7497 0924-669X |
| Popis: | k-means is traditionally viewed as an algorithm for the unsupervised clustering of a heterogeneous population into a number of more homogeneous groups of objects. However, it is not necessarily guaranteed to group the same types (classes) of objects together. In such cases, some supervision is needed to partition objects which have the same label into one cluster. This paper demonstrates how the popular k-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The output field itself cannot be used in the clustering but it is used in developing a suitable metric defined on other fields. The proposed algorithm combines Simulated Annealing with the modified k-means algorithm. We apply the proposed algorithm to real data sets, and compare the output of the resultant classifier to that of C4.5. |
| Databáze: | OpenAIRE |
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