Improvement of k-means Clustering Algorithm for Analyzing the Morphology of Ice Ridge Sails


Contact
Christian.Haas [ at ] ualberta.ca

Abstract

An improved k-means clustering algorithm is proposed after analyzing the disadvantages of the traditional k-means algorithm. The cluster centers are initialized by combining the sample mean and standard deviation, the optimal cluster centers are searched by the hybridizing particle swarm optimization and traditional k-means algorithm, and the criterion function is improved during the iteration process to search the optimal number of clusters. The theory analysis and experimental results show that the improved algorithm not only avoids the local optima, also has greater searching capability than the traditional algorithm. This improved algorithm is used to analyze the morphology of the ridge sail (the upper surface of ice ridges). The comparison with the measured data shows that the influences of the geographical locations and the growing environments on the formation of ice ridges can be perfectly reflected by the clustered results.



Item Type
Article
Authors
Divisions
Programs
Publication Status
Published
Eprint ID
25580
Cite as
Tan, B. , Li, Z. , Lu, P. , Haas, C. and Feng, E. (2011): Improvement of k-means Clustering Algorithm for Analyzing the Morphology of Ice Ridge Sails , International Journal of Advancements in Computing Technology (IJACT), 3 (9), pp. 329-336 .


Download
[thumbnail of TanBingRidgeMorphology040_Oct_2011_IJACT.pdf]
Preview
PDF
TanBingRidgeMorphology040_Oct_2011_IJACT.pdf

Download (904kB) | Preview
Cite this document as:

Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Geographical region

Research Platforms

Campaigns
ANT > XXIII > 7


Actions
Edit Item Edit Item