Clustering of measures via mean measure quantization

K-mean Based Clustering and Context Quantization

In this thesis, we study the problems of K-means clustering and context quantization. The main task of K-means clustering is to partition the training patterns into k distinct groups or clusters that minimize the mean-square-error (MSE) objective function. But the main difficulty of conventional K-means clustering is that its classification performance is highly susceptible to the initialized s...

Fast Color Quantization via Fuzzy Clustering

Speaker clustering via the mean shift algorithm

In this paper, we investigate the use of the mean shift algorithm with respect to speaker clustering. The algorithm is an elegant nonparametric technique that has become very popular in image segmentation, video tracking and other image processing and computer vision tasks. Its primary aim is to detect the modes of the underlying density and consequently merge those observations being attracted...

Similarity Measures and Weighted Fuzzy C-Mean Clustering Algorithm

In this paper we study the fuzzy c-mean clustering algorithm combined with principal components method. Demonstratively analysis indicate that the new clustering method is well rather than some clustering algorithms. We also consider the validity of clustering method. Keywords—FCM algorithm, Principal Components Analysis, ClusI. FUZZY SET THEORETIC SIMILARITY MEASURES ONE of the most important ...

Information Measures via Copula Functions

In applications of differential geometry to problems of parametric inference, the notion of divergence is often used to measure the separation between two parametric densities. Among them, in this paper, we will verify measures such as Kullback-Leibler information, J-divergence, Hellinger distance, -Divergence, … and so on. Properties and results related to distance between probability d...