PSO Optimized Nearest Neighbor Algorithm
Manish Mahajan1, Santosh Kumar2, Bhasker Pant3, Kireet Joshi4, Vikas Tripathi5
1Manish Mahajan, Computer Science and Engg. Deptt. Graphic Era Deemed to be University, Dehradun, India.
2Santosh Kumar, Computer Science and Engg. Deptt. Graphic Era Deemed to be University, Dehradun, India.
3Bhasker Pant, Computer Science and Engg. Deptt. Graphic Era Deemed to be University, Dehradun, India.
4Kireet Joshi, Computer Science and Engg. Deptt. Graphic Era Deemed to be University, Dehradun, India.
5Vikas Tripathi, Computer Science and Engg. Deptt. Graphic Era Deemed to be University, Dehradun, India.
Manuscript received on November 27, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1508-1513 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3574129219/2020©BEIESP | DOI: 10.35940/ijeat.B3574.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data mining can be considered to be an important aspects of information industry. Data mining has found a wide applicability in almost every field which deals with data. Out of the various techniques employed for data mining, Classification is a very commonly used tool for knowledge discovery. Various alternatives methods are available which can be used to create a classification model, out of which the most common and apprehensible one is KNN. In spite of KNN having a number of shortcomings and limitations in it, these can be overcome by with the help of alterations which can be made to the basic KNN algorithm. Due to its wide applicability, kNN has been the focus of extensive research and as a result, many alternatives have been performed with wide range of success in performance improvement. A major hardship being faced by the data mining applications is the large number of dimensions which render most of the data mining algorithms inefficient. The problem can be solved to some extent by using dimensionality reduction methods like PCA. Further improvements in the efficiency of the classification based mining algorithms can be achieved by using optimization methods. Meta-heuristic algorithms inspired by natural phenomenon like particle swarm optimization can be used very effectively for the purpose.
Keywords: Classification, Data mining, k NN, Particle Swarm Optimization, Principal Component Analysis.