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자기 조직화 신경망을 이용한 클러스터링 알고리듬

Title
자기 조직화 신경망을 이용한 클러스터링 알고리듬
Other Titles
A Clustering Algorithm using Self-Organizing Feature Maps
Author
강맹규
Keywords
clustering; Self-Organizing Feature Maps; unsupervised neural network; euclidean distance
Issue Date
2005-09
Publisher
대한산업공학회
Citation
대한산업공학회지, v. 31, No. 3, Page. 257 - 264
Abstract
This paper suggests a heuristic algorithm for the clustering problem. Clustering involves grouping similar objects into a cluster. Clustering is used in a wide variety of fields including data mining, marketing, and biology. Until now there are a lot of approaches using Self-Organizing Feature Maps(SOFMs). But they have problems with a small output-layer nodes and initial weight. For example, one of them is a one-dimension map of k output-layer nodes, if they want to make k clusters. This approach has problems to classify elaboratively. This paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node's weight. We can find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. We use the well known IRIS data as an experimental data. Unsupervised clustering of IRIS data typically results in 15 - 17 clustering error. However, the proposed algorithm has only six clustering errors.
URI
http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE01908586&language=ko_KRhttp://repository.hanyang.ac.kr/handle/20.500.11754/111453
ISSN
1225-0988
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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