The K-means algorithm is a popular data-clustering algorithm. However, one of its
drawbacks is the requirement for the number of clusters, K, to be specified before the …

Learning vector quantization (LVQ) is one of the most powerful approaches for prototype
based classification of vector data, intuitively introduced by Kohonen. The prototype …

Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

Clustering data streams is an interesting Data Mining problem. This article presents three
variants of the K-means algorithm to cluster binary data streams. The variants include On …

A new on-line criterion for identifying “useless” neurons of a self-organizing network is
proposed. When this criterion is used in the context of the (formerly developed) growing …

For pt. I see ibid., p. 775-85. In part I an equivalence between the concepts of fuzzy
clustering and soft competitive learning in clustering algorithms is proposed on the basis of …

Clustering applications cover several fields such as audio and video data compression,
pattern recognition, computer vision, medical image recognition, etc. In this paper, we …

In clustering algorithm, one of the main challenges is to solve the global allocation of the
clusters instead of just local tuning of the partition borders. Despite this, all external cluster …

Several clustering algorithms can be applied to clustering in large multimedia databases.
The effectiveness and efficiency of the existing algorithms, however, are somewhat limited …