A Hybrid SOM-Based Document Organization System
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
Engineering Applications of Artificial Intelligence
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
An intelligent market segmentation system using k-means and particle swarm optimization
Expert Systems with Applications: An International Journal
How Emergent Self Organizing Maps Can Help Counter Domestic Violence
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Integration of ant colony SOM and k-means for clustering analysis
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Self-organizing map initialization
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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Data clustering is an important and widely used task of data mining that groups similar items together into subsets. This paper introduces a new clustering algorithm SOM++, which first uses K-Means++ method to determine the initial weight values and the starting points, and then uses Self-Organizing Map (SOM) to find the final clustering solution. The purpose of this algorithm is to provide a useful technique to improve the solution of the data clustering and data mining in terms of runtime, the rate of unstable data points and internal error. This paper also presents the comparison of our algorithm with simple SOM and K-Means + SOM by using a real world data. The results show that SOM++ has a good performance in stability and significantly outperforms three other methods training time.