The Adept K-Nearest Neighbour Algorithm - An optimization to the Conventional K-Nearest Neighbour Algorithm

  • Anjali Ganesh Jivani The Maharaja Sayajirao University of Baroda
  • Karishma Shah Department of Computer Science and Engineering, The M. S. University of Baroda, Vadodara, India.
  • Shireen Koul Department of Computer Science and Engineering, The M. S. University of Baroda, Vadodara, India.
  • Vidhi Naik Department of Computer Science and Engineering, The M. S. University of Baroda, Vadodara, India
Keywords: Supervised Learning, classification, k Nearest Neighbour, Cosine Similarity

Abstract

This research aims to study the efficiency of a well-known classification algorithm, K-Nearest Neighbour, and suggest a new classification method, an optimised version than one of the existing classification method. The purpose of this research is to reduce the time taken by the existing K- Nearest Neighbour Classification method. The classification algorithm’s purpose is to identify the characteristics that indicate the class to which each document belongs. This pattern not only helps in understanding the existing data but also to predict how new instances will behave. Classification algorithms create classification models by examining already classified data (cases) and inductively finding a predictive pattern.

Author Biography

Anjali Ganesh Jivani, The Maharaja Sayajirao University of Baroda
Computer Science & Engineering

References

(1) Based on study from http://www.academia.edu/4607757/Application_of_K-Nearest_Neighbor_kNN_Approach_for_Predicting_Economic_Events_Theoretical_Background

(2) Biju Issac, ‎Nauman Israr. “ Case Studies in Intelligent Computing: Achievements and Trends”

(3) Altman, N. S. (1992). "An introduction to kernel and nearest-neighbor nonparametric regression".

(4) Antti Ajanki AnAj. http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

(5) Based on the study from Jiawei Han, Micheline Kamber. “Data Mining Concepts and Techniques”

(6) http://research.cs.tamu.edu/prism/lectures/pr/pr_l8.pdf

(7) http://www.nickgillian.com/wiki/pmwiki.php?n=GRT.kNN

Published
2016-03-03