The limitations of using self-organizing maps (SaM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical findings and the relevant theory. SaM 's remaining ability of doing both VQ and MDS at the same time is challenged by a new combined technique of online K-means clustering plus Sammon mapping of the cluster centroi...

Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a med...

In this paper we have investigated the performance of PSO Particle Swarm Optimization based clustering on few real world data sets and one artificial data set. The performances are measured by two metric namely quantization error and inter-cluster distance. The K means clustering algorithm is first implemented for all data sets, the results of which form the basis of comparison of PSO based app...

We study a transductive learning approach based on clustering. In this approach one constructs a diversity of unsupervised models of the unlabeled data using clustering algorithms. These models are then exploited to construct a number of hypotheses using the labeled data and the learner selects an hypothesis that minimizes a transductive PACBayesian error bound, which holds with high probabilit...

The limitations of using self-organizing maps (SOM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical ndings and the relevant theory. SOM's remaining ability of doing both VQ and MDS at the same time is challenged by a new combined technique of online K-means clustering plus Sammon mapping of the cluster centroids....

We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. When compared with the current state-of-the-art, our methods provide a substantial improvement in the time to answer a query for cluster centers, while retaining the desirable properties of provably small approximation error, and low space usage. Our algorithms are based on a no...

We present a novel method for improving hierarchical speaker clustering in the tasks of speaker diarization and speaker linking. In hierarchical clustering, a tree can be formed that demonstrates various levels of clustering. We propose a ratio that expresses the impact of each cluster on the formation of this tree and use this to rescale cluster scores. This provides score normalisation based ...

The effect of the choice of features on unsupervised clustering in audio surveillance is investigated. The importance of individual features in a larger feature set is first analyzed by examining the component loadings in principal component analysis (PCA). The individual sound events are then assigned into clusters using the self-tuning spectral clustering and the classical K-means algorithms....

In this study, MR Image segmentation has been realized with some clustering algorithms. In the study, the performances kmeans, lloyds, llyds-kmeans, pso clustering, ga clustering and jaya optimisation algorithms on some MR images from BRATS 2012 dataset have been compared. For the comparison, the manual segmentation results of MR images from BRATS 2012 dataset have been referenced and results h...

We describe the Hierarchical Classifier (HC), which is a hybrid architecture [1] built with the help of supervised training and unsu-pervised problem clustering. We prove a theorem giving the estimationˆR of HC risk. The proof works because of an improved way of computing cluster weights, introduced in this paper. Experiments show thatˆR is correlated with HC real error. This allows us to usê R...