A Distributed K Nearest Neighbor Classifier for Big Data

Abstract

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

Determination Characteristic and Classification the Types of Orange Using UV-Vis Spectrophotometer by K-Nearest Neighbor Algorithm

“…The classical KNN algorithm is based on calculating the distance between the test data instances to be classified and all of the instances in the training data set and finding the closest K number of training instances. After detecting the K number of closest training instances, the KNN algorithm applies majority voting which is the process of detecting the data class with the maximum number of instances among the K selected instances [6].…”

“…Since the classical KNN algorithm is completely based on individual instance proximities, it heavily suffers from high computation cost. In addition, since the algorithm decisionmaking strategy is relying on the individual instance proximities rather than stronger class representations, the algorithm's classification accuracy is also not adequate for modern data big analysis that requires rapid and accurate classification results [6].…”