Open AccessPerformance Analysis of KMeans and KMediods Algorithms in Air Pollution Prediction
Author(s) -
S. Suganya,
Dr.T. Meyyappan,
Sandeep Kumar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6495.018520
Subject(s) - air pollution , pollution , cluster analysis , atmosphere (unit) , computer science , data mining , algorithm , environmental science , meteorology , machine learning , geography , ecology , chemistry , organic chemistry , biology
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.