Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCIS.2006.252268
DC FieldValue
dc.titleRecursive self organizing maps with hybrid clustering
dc.contributor.authorRamanathan, K.
dc.contributor.authorGuan, S.U.
dc.date.accessioned2014-06-19T03:25:26Z
dc.date.available2014-06-19T03:25:26Z
dc.date.issued2006
dc.identifier.citationRamanathan, K.,Guan, S.U. (2006). Recursive self organizing maps with hybrid clustering. 2006 IEEE Conference on Cybernetics and Intelligent Systems : -. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICCIS.2006.252268" target="_blank">https://doi.org/10.1109/ICCIS.2006.252268</a>
dc.identifier.isbn1424400236
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71586
dc.description.abstractWe 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICCIS.2006.252268
dc.sourceScopus
dc.subjectClustering
dc.subjectEnsemble approaches
dc.subjectGenetic algorithms
dc.subjectHybrid learning
dc.subjectTask decomposition
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICCIS.2006.252268
dc.description.sourcetitle2006 IEEE Conference on Cybernetics and Intelligent Systems
dc.description.page-
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

4
checked on May 10, 2022

Page view(s)

79
checked on May 12, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.