JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Mixed Co-clustering Algorithm Based on Information Bottleneck |
| 영문제목(English Title) |
A Mixed Co-clustering Algorithm Based on Information Bottleneck |
| 저자(Author) |
Cao Kerang
Hyunju Lee
Hoekyung Jung
Yongli Liu
Tianyi Duan
Xing Wan
Hao Chao
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| 원문수록처(Citation) |
VOL 13 NO. 06 PP. 1467 ~ 1486 (2017. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clustering relaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzy co-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy coclustering and possibilistic clustering, and formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and feature cluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimental results show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI, in terms of accuracy and robustness.
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| 키워드(Keyword) |
IoT
Priority
Sensor
Smart Home
Task Management System
Co-clustering
F-Measure
Fuzzy Clustering
Information Bottleneck
Objective Function
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