Name
Abstract | ||
|---|---|---|
In this paper we present a new approach for clustering data. The clustering metric used is the normalized cross- correlation, also known as similarity, instead of the traditionally used Euclidean distance. The main advantage of this metric is that it depends on the signal shape rather than its amplitude. Under an assumption of an exponential probability model that has several desirable properties, the expectation-maximization (EM) framework is used to derive two iterative clustering algorithms. Numerical experiments are presented using simulated data in a dynamic positron emission topography study of the brain. Initial results demonstrate that the proposed method achieves better performance than several existing clustering methods. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICIP.2002.1037968 | ICIP (1) |
Keywords | Field | DocType |
maximum likelihood estimation,parameter estimation,pet,expectation maximization,expectation maximization algorithm,signal to noise ratio,image classification,shape,iterative methods,euclidean distance,surfaces,radioactive decay,normalized cross correlation,clustering algorithms | k-medians clustering,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis | Conference |
Citations | PageRank | References |
1 | 0.47 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jovan G. Brankov | 1 | 82 | 12.09 |
| Yongyi Yang | 2 | 1409 | 140.74 |
| Nikolas P. Galatsanos | 3 | 632 | 52.16 |
| Miles N. Wernick | 4 | 595 | 61.13 |