We propose a parallel graph-based data clustering algorithm using CUDA GPU, based on exact clustering of the minimum spanning tree in terms of a minimum isoperimetric criteria. We also provide a comparative performance analysis of our algorithm with other related ones which demonstrates the general superiority of this parallel algorithm over other competing algorithms in terms of accuracy and speed.
Various algorithms and strategies have been proposed to improve the computational performance of data clustering and its applications to image segmentation, signal processing and etc. In this manner, some attempts have been devoted to develop parallel adaptations of the well-known methods. K-means is one of the most popular algorithm in data clustering literature. Firstly, Stoffel and Belkoniene @cite_15 developed some parallel techniques for this algorithm. Afterwards, many attempts was devoted to design and implement the parallel version for k-means in the large data sets (e.g. see @cite_5 and @cite_4 ).
@cite_5 @cite_15 @cite_4
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Developing fast algorithms for clustering has been an important area of research in data mining and other fields. K-means is one of the widely used clustering algorithms. In this work, we have developed and evaluated parallelization of k-means method for low-dimensional data on message passing computers. Three different data decomposition schemes and their impact on the pruning of distance calculations in tree-based k-means algorithm have been studied. Random pattern decomposition has good load balancing but fails to prune distance calculations effectively. Compact spatial decomposition of patterns based on space filling curves outperforms random pattern decomposition even though it has load imbalance problem. In both cases, parallel tree-based k-means clustering runs significantly faster than the traditional parallel k-means. The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a very simple, randomized seeding technique, we obtain an algorithm that is Θ(logk)-competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both the speed and the accuracy of k-means, often quite dramatically. The k-means algorithm is by far the most widely used method for discovering clusters in data. We show how to accelerate it dramatically, while still always computing exactly the same result as the standard algorithm. The accelerated algorithm avoids unnecessary distance calculations by applying the triangle inequality in two different ways, and by keeping track of lower and upper bounds for distances between points and centers. Experiments show that the new algorithm is effective for datasets with up to 1000 dimensions, and becomes more and more effective as the number k of clusters increases. For k ≥ 20 it is many times faster than the best previously known accelerated k-means method. K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present an overview of these methods with an emphasis on their computational efficiency. We then compare eight commonly used linear time complexity initialization methods on a large and diverse collection of data sets using various performance criteria. Finally, we analyze the experimental results using non-parametric statistical tests and provide recommendations for practitioners. We demonstrate that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods.