SOM++: Integration of self-organizing map and K-Means++ algorithms

Doğan Y., Birant D., Kut R. A.

9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013, New York, United States Of America, 19 - 25 July 2013, pp.246-259 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1007/978-3-642-39712-7_19
  • City: New York
  • Country: United States Of America
  • Page Numbers: pp.246-259
  • Keywords: Clustering, Data Mining, K-Means++, Mining Methods and Algorithms, Self-Organizing Map
  • Dokuz Eylül University Affiliated: Yes


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. © 2013 Springer-Verlag.