improvement of the proposed method, several experiments were carried out using different real datasets. The presented results, which are achieved after extensive experiments, prove that the proposed algorithm improves the classification accuracy of KMeans. The achieved performance was also compared against several recent classification studies which are based on different classification schemes.

The use of hybrid algorithms for solving real-world optimization problems has become popular since their solution quality can be made better than the algorithms that form them by combining their desirable features. The newly proposed hybrid method which is called Hybrid Differential, Particle, and Harmony (HDPH) algorithm is different from the other hybrid forms since it uses all features of merged algorithms in order to perform efficiently for a wide variety of problems. In the proposed algorithm the control parameters are randomized which makes its implementation easy and provides a fast response. This paper describes the application of HDPH algorithm to linear antenna array synthesis. The results obtained with the HDPH algorithm are compared with three merged optimization techniques that are used in HDPH. The comparison shows that the performance of the proposed algorithm is comparatively better in both solution quality and robustness. The proposed hybrid algorithm HDPH can be an efficient candidate for real-time optimization problems since it yields reliable performance at all times when it gets executed.

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The K-Nearest Neighbor classifier is a well-known and widely applied method in data mining applications. Nevertheless, its high computation and memory usage cost makes the classical K-NN not feasible for today's Big Data analysis applications. To overcome the cost drawbacks of the known data mining methods, several distributed environment alternatives have emerged. Among these alternatives, Hadoop MapReduce distributed ecosystem attracted significant attention. Recently, several K-NN based classification algorithms have been proposed which are distributed methods tested in Hadoop environment and suitable for emerging data analysis needs. In this work, a new distributed Z-KNN algorithm is proposed, which improves the classification accuracy performance of the well-known K-Nearest Neighbor (K-NN) algorithm by benefiting from the representativeness relationship of the instances belonging to different data classes. The proposed algorithm relies on the data class representations derived from the Z data instances from each class, which are the closest to the test instance. The Z-KNN algorithm was tested in a physical Hadoop Cluster using several real-datasets belonging to different application areas. The performance results acquired after extensive experiments are presented in this paper and they prove that the proposed Z-KNN algorithm is a competitive alternative to other studies recently proposed in the literature