Average Competitive Learning Vector Quantization

An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering

This letter derives a new interpretation for a family of competitive learning algorithms and investigates their relationship to fuzzy c-means and fuzzy learning vector quantization. These algorithms map a set of feature vectors into a set of prototypes associated with a competitive network that performs unsupervised learning. Derivation of the new algorithms is accomplished by minimizing an ave...

Adaptive Vector Quantization with Deletion Method Based on Equinumber of Inputs in Partition Space

This paper presents an adaptive vector quantization with deletion method based on equinumber of inputs in partition space. Then we exhibit that partition errors are equivalent to each other when the numbers of inputs in partition space are mutually same, and describe that the average distortion is asymptotically minimized. We present an algorithm from the viewpoint of the equinumber principle a...

On the critical points of the 1-dimensional competitive learning vector quantization algorithm

NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...

Pursuit Reinforcement Competitive Learning: PRCL based Online Clustering with Learning Automata

A new online clustering method based on not only reinforcement and competitive learning but also pursuit algorithm (Pursuit Reinforcement Competitive Learning: PRCL) as well as learning automata is proposed for reaching a relatively stable clustering solution in comparatively short time duration. UCI repository data which are widely used for evaluation of clustering performance in usual is used...