Affiliations: [a] Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India
| [b] Department of Information Technology, Manipal University Jaipur, Jaipur, India
Correspondence:
[*]
Corresponding author. Ashish Kumar, Department of Computer Science and Engineering, Manipal University Jaipur, 303007 Jaipur, India. E-mail:kumar.ashish@jaipur.manipal.edu.
Abstract: Even though finding out distance is the central core of k-Nearest Neighbor classification techniques, similarity measures are often favored against distance in various realistic scenarios and situation. Most of the similarity measures, which are used to classify an instance, are based on geometric model. Their effectiveness decreases with the increases in the number of dimensions. This paper establishes an efficient technique called ARSkNN for finding out class of any given instance using a measure based on an unique similarity, that does no longer compute distance, for k-NN classification. Our empirical results show that ARSkNN classification technique is better than the previous established k-NN classifiers. The performance of algorithm was verified and validated on various datasets from different domains.
Keywords: Data mining, classification, nearest neighbor, similarity measure
