K-means is a widely used iterative clustering algorithm. There has been considerable work on improving k-means in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose two heuristics to overcome this bottleneck and speed up k-means. Our first heuristic predicts...

We present a novel unsupervised algorithm for quickly finding clusters in multidimensional data. It does not make the assumption of isotropy, instead taking full advantage of the anisotropic Gaussian kernel, to adapt to local data shape and scale. We employ some little-used properties of the multivariate Gaussian distribution to represent the data, and also give, as a corollary of the theory we...

The Finger-to-nose test (FNT) is an accepted neurological evaluation to study the coordination conditions. In this work, a methodology for the analysis of data from FNT is proposed, aimed at assessing the evolution of the condition of Spinocerebellar Ataxia type 2 (SCA2) patients. First of all, test results obtained from both patients and healthy individuals are processed through principal comp...

This paper introduces a hybrid approach that is based on color information that utilizes background subtraction technique, a mask and K-Mean clustering algorithm. This hybrid approach efficiently removes artifacts caused by lightening changes such as highlight, reflection, and shadows of moving objects from segmentation. We first create a mask by assigning values to R, G and B channels utilizin...

Since advanced technologies via social media, internet, virtual communities and networks internet of things (IoT), there are more multi-view data to be collected. Multi-view clustering is a substantial tool as natural design for data. K-means (KM) (single-view) had been extended handling data, called KM (MV-KM). In the literature, most MV-KM algorithms reported influenced by initializations als...

This study has applied digital image processing on three-dimensional C.T. images to detect and diagnose kidney diseases. Medical of different cases diseases were compared with those healthy cases. Four kidneys disorders, such as stones, tumors (cancer), cysts, renal fibrosis considered in additional tissues. method helps differentiating between the diseased It can its very early stages, before ...