Volume 19, No. 5, 2022

An Efficient Density Clustering Based Ensemble Classification Learning Model For Large Real-Time Spatial Aqi Database


A.Nageswara Rao , Dr. Bendi Venkata Ramana

Abstract

Air quality analysis plays a vital role in the multi-region based severity detection. Since, most of the conventional density based clustering approaches use static homogeneous type of air quality data for severity prediction. However, most of the conventional models are not applicable to dynamic sub-region based cluster analysis for severity prediction. In this work,a novel weighted density inter and intra cluster based ensemble learning approach is developed for air quality prediction process. Experimental results show that the proposed multi-level weighted density based clustering approach has better efficiency for sub-clustering and severity detection process than the conventional approaches.


Pages: 778-790

Keywords: Air quality analysis, multi-level clustering, heterogeneous data samples.

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