Optimized KNN for sequential execution

K-nearest neighbor is widely used data mining algorithm in many areas such as text, medicine. In this research, methods to speed up the K-nearest neighbor algorithm when executed sequentially have been proposed. All of these methods scan through the data set element by element. The first one uses a max heap implementation to speed up finding nearest neighbors of a data point. The second one uses a modified version of randomized selection to find the kth order statistic and then subsequently prune the data points to get the neighbors. The third method uses an insertion mechanism to find the appropriate position every time a data point is scanned in the data set in an initially maintained k-sized sorted array. Based on the size of the data set and the value of k, a suitable method is chosen from the above mentioned methods. Analysis of the methods for various data set sizes and k values has also been performed.