Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 4 Articles
Rapid growth of high dimensional datasets in recent years has created an emergent need to extract the knowledge underlying them. Clustering is the process of automatically finding groups of similar data points in the space of the dimensions or attributes of a dataset. Finding clusters in the high dimensional datasets is an important and challenging data mining problem. Data group together differently under different subsets of dimensions, called subspaces. Quite often a dataset can be better understood by clustering it in its subspaces, a process called subspace clustering. But the exponential growth in the number of these subspaces with the dimensionality of data makes the whole process of subspace clustering computationally very expensive. There is a growing demand for efficient and scalable subspace clustering solutions in many Big data application domains like biology, computer vision, astronomy and social networking. Apriori based hierarchical clustering is a promising approach to find all possible higher dimensional subspace clusters from the lower dimensional clusters using a bottom-up process. However, the performance of the existing algorithms based on this approach deteriorates drastically with the increase in the number of dimensions. Most of these algorithms require multiple database scans and generate a large number of redundant subspace clusters, either implicitly or explicitly, during the clustering process. In this paper, we present SUBSCALE, a novel clustering algorithm to find non-trivial subspace clusters with minimal cost and it requires only k database scans for a k-dimensional data set. Our algorithm scales very well with the dimensionality of the dataset and is highly parallelizable. We present the details of the SUBSCALE algorithm and its evaluation in this paper....
The purpose of this paper is to present accelerations of the Mann and CQ algorithms. We first apply the Picard algorithm to the smooth convex minimization problem and point out that the Picard algorithm is the steepest descent method for solving the minimization problem. Next, we provide the accelerated Picard algorithm by using the ideas of conjugate gradient methods that accelerate the steepest descent method. Then, based on the accelerated Picard algorithm, we present accelerations of the Mann and CQ algorithms. Under certain assumptions, we show that the new algorithms converge to a fixed point of a nonexpansive mapping. Finally, we show the efficiency of the accelerated Mann algorithm by numerically comparing with the Mann algorithm. A numerical example is provided to illustrate that the acceleration of the CQ algorithm is ineffective....
Meta-heuristic algorithms proved to find optimal solutions for combinatorial problems in many\ndomains. Nevertheless, the efficiency of these algorithms highly depends on their parameter settings.\nIn fact, finding appropriate settings of the algorithm�s parameters is considered to be a nontrivial\ntask and is usually set manually to values that are known to give reasonable performance.\nIn this paper, Ant Colony Optimization with Parametric Analysis (ACO-PA) is developed to overcome\nthis drawback. The main feature of the ACO-PA is the ability of deciding the appropriate parameter\nvalues within the predefined parameter variations. Besides, a new approach which enables\nthe pheromone information value to be proportional to the heuristic information value is\nintroduced. The effectiveness of the proposed algorithm is investigated through the application of\nthe algorithm to the construction site layout problems taken from the state-of-art. Results show\nthat the ACO-PA can reduce transportation cost up to 16.8% compared to the site layouts generated\nby Genetic Algorithms and basic ACO. Moreover, the effects of parameter settings on the generated\nsolutions are investigated....
This paper presents a new algorithm for solving unit commitment (UC) problems using a binaryreal\ncoded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear\nmixed-integer optimization problem, encountered as one of the toughest problems in power systems,\nin which some power generating units are to be scheduled in such a way that the forecasted\ndemand is met at minimum production cost over a time horizon. In the proposed algorithm, the\nalgorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means\nclustering technique. The binary coded GA is used to obtain a feasible commitment schedule for\neach generating unit; while the power amounts generated by committed units are determined by\nusing real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm\ndivides population into a specific number of subpopulations with dynamic size. In this\nway, using k-means clustering algorithm allows the use of different GA operators with the whole\npopulation and avoids the local problem minima. The effectiveness of the proposed technique is\nvalidated on a test power system available in the literature. The proposed algorithm performance\nis found quite satisfactory in comparison with the previously reported results....
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