Cluster analysis and fuzzy query in ship maintenance and design

Authors:
Jianhua Che;Qinming He;Yinggang Zhao;Feng Qian;Qi Chen
Affiliations:
College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Venue:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

Citing 6
Cited 0

An Efficient k-Means Clustering Algorithm: Analysis and Implementation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy query interface for a business database

International Journal of Human-Computer Studies
K-means clustering versus validation measures: a data distribution perspective

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Graph Cuts without Eigenvectors A Multilevel Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new graph-theoretic approach to clustering and segmentation

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cluster analysis and fuzzy query win wide-spread applications in modern intelligent information processing. In allusion to the features of ship maintenance data, a variant of hypergraph-based clustering algorithm, i.e., Correlation Coefficient-based Minimal Spanning Tree(CC-MST), is proposed to analyze the bulky data rooting in ship maintenance process, discovery the unknown rules and help ship maintainers make a decision on various device fault causes. At the same time, revising or renewing an existed design of ship or device maybe necessary to eliminate those device faults. For the sake of offering ship designers some valuable hints, a fuzzy query mechanism is designed to retrieve the useful information from large-scale complicated and reluctant ship technical and testing data. Finally, two experiments based on a real ship device fault statistical dataset validate the flexibility and efficiency of the CC-MST algorithm. A fuzzy query prototype demonstrates the usability of our fuzzy query mechanism.