Semi-supervised Clustering Based on K-Nearest Neighbors

Author

Shieh, Horng-Lin

Author_Institution

Dept. of Electr. Eng., St. John´´s Univ., Taipei, Taiwan

fYear

2012

fDate

July 31 2012-Aug. 2 2012

Firstpage

759

Lastpage

762

Abstract

In this study, a semi-supervised clustering algorithm, based on k-nearest neighbors (k-NN), is proposed. The distance relationships between unlabeled and k-nearest neighbor data of each cluster are adopted in order to categorize the unlabeled data. Experiment result shows that proposed method can obtain a good performance.

Keywords

learning (artificial intelligence); pattern clustering; distance relationships; k-NN; k-nearest neighbors; semi-supervised clustering algorithm; unlabeled data; Approximation algorithms; Classification algorithms; Clustering algorithms; Iris recognition; Partitioning algorithms; Pattern recognition; Power cables; k-NN; k-nearest neighbor; semi-supervised clustering algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Manufacturing and Automation (ICDMA), 2012 Third International Conference on

Conference_Location

GuiLin

Print_ISBN

978-1-4673-2217-1

Type

conf

DOI

10.1109/ICDMA.2012.179

Filename

6298627

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3299258