Performance Analysis of KMeans and KMediods Algorithms in Air Pollution Prediction
S. Suganya1, T. Meyyappan2, S. Santhosh Kumar3
1S. Suganya*, Department of computer Science, Alagappa University, Karaikudi , Tamil Nadu, India.
2Dr. T.Meyyappan, Department of computer Science, Alagappa University, Karaikudi, Tamil Nadu, India.
3Dr. S. Santhosh Kumar, Department of computer Science, Alagappa University, Karaikudi, Tamil Nadu, India. 

Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3573-3577 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6495018520/2020©BEIESP | DOI: 10.35940/ijrte.E6495.018520

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Abstract: Air pollution is a major part of human health problems in many cities. Air pollution can cause many negative effects on the environment. The most basic solution for air pollution is humane should have responsible habits and also have to use more efficient devices to predict and control the atmosphere pollution. The nearly everyone important objective of this effort is to analyze and predict the atmosphere smog using data mining techniques. And help to take inevitable carriage steps or decision for protect the future generation from the rapid increase of air pollution. To turn raw data into useful information, Data mining technique is used by many companies. Data mining extract the hidden useful information from the air pollution dataset. It helps and supporting human making decision human, being as responsible, and control/ avoids the air pollution as claimed by the severe level of pollution in the airspace city based. To analyze and predict simple and efficient clustering techniques such as K-Means and K-Medoids has been used.
Keywords: Air pollution, Clustering, Algorithm K-Means, K-mean Medoids.
Scope of the Article: Parallel and Distributed Algorithms.