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PAKDD
2005
ACM

Dynamic Cluster Formation Using Level Set Methods

10 years 9 months ago
Dynamic Cluster Formation Using Level Set Methods
Density-based clustering has the advantages for (i) allowing arbitrary shape of cluster and (ii) 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. In higher dimension, the boundaries become wiggly and over-fitting often occurs. 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 as clusters touch each other, CIFs still clearly reveal cluster centers, cluster boundaries, degree of membership of each data point to the cluster that it belongs, and, whether a certain data point is an outlier or not. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on kernel density functions which are often oscillatory or overs...
Andy M. Yip, Chris H. Q. Ding, Tony F. Chan
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PAKDD
Authors Andy M. Yip, Chris H. Q. Ding, Tony F. Chan
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