| Autor: |
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Gervasi, Osvaldo, Gavrilova, Marina L., Pérez O, Joaquín, Pazos R, Rodolfo, Cruz R, Laura |
| Zdroj: |
Computational Science & Its Applications - ICCSA 2007; 2007, p674-682, 9p |
| Abstrakt: |
Clustering problems arise in many different applications: machine learning, data mining, knowledge discovery, data compression, vector quantization, pattern recognition and pattern classification. One of the most popular and widely studied clustering methods is K-means. Several improvements to the standard K-means algorithm have been carried out, most of them related to the initial parameter values. In contrast, this article proposes an improvement using a new convergence condition that consists of stopping the execution when a local optimum is found or no more object exchanges among groups can be performed. For assessing the improvement attained, the modified algorithm (Early Stop K-means) was tested on six databases of the UCI repository, and the results were compared against SPSS, Weka and the standard K-means algorithm. Experimentally Early Stop K-means obtained important reductions in the number of iterations and improvements in the solution quality with respect to the other algorithms. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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