This paper reports a comparison of calculated molecular properties and of 2D fragment bit-strings when used for the selection of structurally diverse subsets of a file of 44295 compounds. MaxMin dissimilarity-based selection and k-means clusterbased selection are used to select subsets containing between 1% and 20% of the file. Investigation of the numbers of bioactive molecules in the selected...

Minkowski Weighted K-Means is a variant of K-Means set in the Minkowski space, automatically computing weights for features at each cluster. As a variant of K-Means, its accuracy heavily depends on the initial centroids fed to it. In this paper we discuss our experiments comparing six initializations, random and five other initializations in the Minkowski space, in terms of their accuracy, proc...

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June 24, 2010 Type Package Title Spherical k-Means Clustering Version 0.1-5 Author Kurt Hornik, Ingo Feinerer, Martin Kober Maintainer Kurt Hornik Description Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a simple fixed-point algorithm and an interface to the CLUTO vcluster program. License GPL-2 Imports slam (>...

In this paper we propose a tri-stage cluster identification model that is a combination of a simple single iteration di tance algorithm and an iterative K-means algorithm. In this study of earthquake seismicity, the model considers event location, time and magnitude information from earthquake catalog data to efficiently classify events as either background or mainshock and aftershock sequences...

The K-Means Clustering Approach is one of main algorithms in the literature of Pattern recognition and Machine Learning. Yet, due to the random selection of cluster centers and the adherence of results to initial cluster centers, the risk of trapping into local optimality ever exists. In this paper, inspired by a genetic algorithm which is based on the K-means method , a new approach is develop...

and showed the solution G is the leading eigenvectors of the symmetric positive semi definite matrix K. When K = AA> (sample covariance matrix) with A = [x1, ...,xm], xi ∈ Rn, those eigenvectors form a basis to a k-dimensional subspace of Rn which is the closest (in L2 norm sense) to the sample points xi. The axes (called principal axes) g1, ...,gk preserve the variance of the original data in ...

Despite its simplicity and its linear time, a serial K-means algorithm's time complexity remains expensive when it is applied to a problem of large size of multidimensional vectors. In this paper we show an improvement by a factor of O(K/2), where K is the number of desired clusters, by applying theories of parallel computing to the algorithm. In addition to time improvement, the parallel versi...