Self-organized chains for clustering

This paper presents a new algorithm for clustering. It is an generalisation of the K-means algorithms . Each cluster will be represented by a chain of prototypes instead of being represented by one prototype like for the K-means. The chains are competing together to represent clusters and are evolving according to Kohonen maps adaptation rule. It is well known that K-means performs very well with hyper-spherical data and has difficulties in dealing with irregular data. We have shown on special artificial data that the new algorithm we are presenting performs very well for different types of data sets . In addition, it shows robustness regarding initial conditions.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Hassan Ghaziri
• Keywords: ESANN 2007, K-means Algorithms, Maps Adaptation Rule, Neural Networks, Special Artificial Data