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 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

Abstract. One of the most important challenges in cellular networks is to utilize the scarce spectrum allocated to the network in the most efficient way. If the channels are statically allocated to the cells, when a large number of mobile hosts move to the cell, that cell may run out of channels resulting in a high call incompletion rate. To overcome this problem, dynamic channel allocation schemes have been proposed. Among these schemes, distributed dynamic channel allocation approaches resulted in good performance results. Nevertheless, distributed allocation schemes must address the problem of efficient co-channel interference avoidance and reducing messaging overhead issues. In this paper, we introduced a new distributed channel allocation scheme namely the DonorList approach, which decreases the amount of messages required per channel allocation while efficiently handling the co-channel interference problem. We also demonstrate the performance results obtained after extensive simulation studies. The results show that the proposed algorithm outperforms the other algorithms recently proposed in the literature.

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