Abstract: The data size is growing day by day as it has large use in industrial applications. Due to various data sizes and type, it creates interrupt to find the exact results. The K Nearest neighbor search technique is widely used to find a similar type of data, but it will result in high computational time as the data size increases. In this research, the various widths clustering is introduced to efficiently find the K Nearest Neighbor (K-NN) for a query object from a given data set. This reduces clustering time in addition to balancing the number of producing clusters and their respective sizes.
Keywords: Clustering, K-nearest neighbor, Various widths clustering, high dimensional
| DOI: 10.17148/IJARCCE.2019.8111