kNNVWC: An efficient k-nearest neighbours approach based on Various-Widths Clustering
Abdulmohsen Almalawi, Adil Fahad, Zahir Tari, Muhammad Aamir Cheema, Ibrahim Khalil · 2016
In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-ofart of k-NN search algorithms.