Clustering in data mining is very important to discover distribution patterns and this importance tends to increase as the amount of data grows. It is one of the main analytical methods in data mining and its method influences its results directly. K-means is a typical clustering algorithm [3]. It mainly consists of two phases i.e. initializing random clusters and to find the nearest neighbour. Both phases have some shortcomings which are discussed in the paper and two methods are purposed based on that. First one is about how to generate the centroids and the second one will reduce the time while calculating distance from centroid.
Automatic target tracking systems are employed in a wide variety of missions and tracking environment such as fire control, guidance, navigation, passive range estimation, and automatic target discrimination. The tracker performance depends upon target size, contrast, speed, and signal-to-noise ratio. The evaluation of a tracker system involves lengthy field trials and measurements. In the present article, a method for quick evaluation of tracker system and working out selection criteria for different tracking algorithm for various target and background combinations have been suggested. Performance measures such as aiming point error, duration of successful tracking, number of tracking losses, indication of confidence, and system reaction time have been used to evaluate the performance of a tracking system.
In this paper we identify various inaccuracies in the paper by R. R. Saxena and S. R. Arora, A Linearization technique for solving the Quadratic Set Covering Problem, Optimization, 39 (1997) 33-42. In particular, we observe that their algorithm does not guarantee optimality, contrary to what is claimed. Experimental analysis has been carried out to assess the value of this algorithm as a heuristic. The results disclose that for some classes of problems the Saxena-Arora algorithm is effective in achieving good quality solutions while for some other classes of problems, its performance is poor. We also discuss similar inaccuracies in another related paper.
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