Generalized clustering networks and Kohonen's self-organizing scheme

Abstract

The relationship between the sequential hard c-means (SHCM) and learning vector quantization (LVQ) clustering algorithms is discussed. The impact and interaction of these two families of methods with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method but often lends ideas to clustering algorithms, are considered. A generalization of LVQ that updates all nodes for a given input vector is proposed. The network attempts to find a minimum of a well-defined objective function. The learning rules depend on the degree of distance match to the winner node; the lesser the degree of match with the winner, the greater the impact on nonwinner nodes. Numerical results indicate that the terminal prototypes generated by this modification of LVQ are generally insensitive to initialization and independent of any choice of learning coefficient. IRIS data obtained by E. Anderson's (1939) is used to illustrate the proposed method. Results are compared with the standard LVQ approach.