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Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a… Expand The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into… Expand The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in… Expand The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values… Expand Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space… Expand Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means… Expand We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center… Expand In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to… Expand Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the… Expand Practical approaches to clustering use an iterative procedure (e.g. K-Means, EM) which converges to one of numerous local minima… Expand