Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPAMI.2006.117
Title: Dynamic cluster formation using level set methods
Authors: Yip, A.M. 
Ding, C.
Chan, T.F.
Keywords: Cluster contours
Cluster intensity functions
Dynamic clustering
Kernel density estimation
Level set methods
Partial differential equations
Issue Date: Jun-2006
Source: Yip, A.M., Ding, C., Chan, T.F. (2006-06). Dynamic cluster formation using level set methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (6) : 877-889. ScholarBank@NUS Repository. https://doi.org/10.1109/TPAMI.2006.117
Abstract: Density-based clustering has the advantages for 1) allowing arbitrary shape of cluster and 2) not requiring the number of clusters as input. However, when clusters touch each other, both the cluster centers and cluster boundaries (as the peaks and valleys of the density distribution) become fuzzy and difficult to determine. We introduce the notion of cluster intensity function (CIF) which captures the important characteristics of clusters. When clusters are well-separated, CIFs are similar to density functions. But, when clusters become closed to each other, CIFs still clearly reveal cluster centers, cluster boundaries, and degree of membership of each data point to the cluster that it belongs. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on density functions obtained by kernel density estimation, which are often oscillatory or oversmoothed. These problems of kernel density estimation are resolved using Level Set Methods and related techniques. Comparisons with two existing density-based methods, valley seeking and DBSCAN, are presented which illustrate the advantages of our approach. © 2006 IEEE.
Source Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/103164
ISSN: 01628828
DOI: 10.1109/TPAMI.2006.117
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

45
checked on Feb 22, 2018

WEB OF SCIENCETM
Citations

35
checked on Jan 22, 2018

Page view(s)

21
checked on Feb 19, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.