This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on multiple weighted norms to measure the distance between the feature vectors and their prototypes. Clustering and LVQ are formulated in this paper as the minimization of a reformulation function that employs distinct weighted norms to measure the distance between each of the prot...

Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting into the objective function. We first formulate the membership and feature weighting, ...

This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We propose an adaptive clustering method based on a hierarchical agglomerative approach, Hierarchical Adaptive Clustering (HAC), that adjusts the partitioning into clusters that was established by applying the hierarchical a...

Clustering moving objects is a challenging task, especially when space consumption must be flexibly and efficiently adjusted for adapting to dynamic object movements. In this paper we develop an efficient approach for managing moving objects and predicting the essential time when moving clusters may need to be updated. Under our approach, moving objects are first inserted into a moving clusteri...

Motivated by colour constancy work in physics-based vision, we develop a new low-dimensional video frame feature that is effectively insensitive to lighting change and apply the feature to keyframe production using hierarchical clustering. The new image feature results from normalising colour channels for frames and then treating 2D histograms of chromaticity as images and compressing these. Be...

In the high dimensional data set having features selection involves identifying a subset of the most useful features that produce compatible results as the original entire set of features. A fast algorithm may be evaluated from both the ability concerns the time required to find a subset of features and the value is required to the quality of the subset of features. Fast clustering based featur...