Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCIS.2006.252268
Title: Recursive self organizing maps with hybrid clustering
Authors: Ramanathan, K.
Guan, S.U. 
Keywords: Clustering
Ensemble approaches
Genetic algorithms
Hybrid learning
Task decomposition
Issue Date: 2006
Source: Ramanathan, K.,Guan, S.U. (2006). Recursive self organizing maps with hybrid clustering. 2006 IEEE Conference on Cybernetics and Intelligent Systems : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCIS.2006.252268
Abstract: We introduce the concept of a neural network based recursive clustering which creates an ensemble of clusters by recursive decomposition of data. The work involves a hybrid combination of a global clustering algorithm followed by a corresponding local clustering algorithm. Evolutionary Self Organizing Maps are used to create clusters. A set of core patterns is isolated and separately trained using a SOM. The process is recursively applied to the remaining patterns to create an ensemble of clusters. The partition of each recursion is integrated with the partition of the previous recursion. The correlation of the clusters with ground truth information (in the form of class labels) is used to measure algorithm robustness. The paper shows that a hybrid combination of evolutionary algorithms and neural network based clustering techniques is efficient in finding good partitions of clusters and in finding suitable resultant cluster shapes. The recursive self organizing map proposed aims to improve the clustering accuracy of the self organizing map. Empirical studies show that superior results are obtained when clustering artificially generated data as well as real world problems such as the Iris, Glass and Wine datasets. ©2006 IEEE.
Source Title: 2006 IEEE Conference on Cybernetics and Intelligent Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/71586
ISBN: 1424400236
DOI: 10.1109/ICCIS.2006.252268
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