Abstract
Within the field of data mining and machine learning, the K-Nearest Neighbor algorithm is a classic algorithm which simply yet elegantly classifies data based upon its similarity to other data. While it follows that the accuracy increases as more data are provided, handling large sets of data is difficult to process serially. It is therefore ideal to perform these tasks in parallel or distributed mode. In this paper, we proposed a framework for distributed nearest neighbor classification. A custom K-Nearest Neighbor algorithm was developed using Hadoop, an environment for developing and deploying applications in parallel on a cluster. The algorithm was implemented on a cluster then tested for accuracy and time of execution. It was observed that the accuracy depends on the provided k-value and on the data set, which is to be expected for the K-Nearest Neighbor process. The time of execution was found to increase logarithmically as the file size, and thus the amount of data the algorithm must parse, increases exponentially.