Pattern classification by combining single feature clusterings = 단일 feature의 clustering 을 이용한 pattern classification 방법에 관한 연구

Departing from traditional clustering algorithms based on distance in a high dimensional feature space, a new clustering method that combines single feature clusterings is developed. The new scheme separates samples into an unknown number of classes considering only one feature at a time. Then the quality of each single feature clustering is measured and used to estimate the feature``s discriminant power. These featurewise clusterings are combined into a global clustering, each contributing by the relative amount of its quality. Due to the subdivision of classification task into simpler single feature clusterings, the algorithm is simpler without normalizing different feature measurements. For the same reason, the clustering procedure and the criterion function are more domain-independent. Moreover the clustering quality of each feature can be used as an estimate of its discriminant power for further tasks such as the classifier design.
Advisors
Kim, Jin-Hyungresearcher김진형researcher
Publisher
한국과학기술원
Issue Date
1987
Identifier
65676/325007 / 000851203
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학과, 1987.2, [ 1책(면수복잡) ]

URI
http://hdl.handle.net/10203/33733
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=65676&flag=t
Appears in Collection
CS-Theses_Master(석사논문)
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