Data mining is an inevitable task in most of the emerging computing technologies as it debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the efficacy to envisage ingeniously the vast and heterogeneous datasets and thus delineates substantial knowledge from the abundance of data by pragmatic implementation of suitable algorithm. There are galore of algorithms in literature for this purpose. Furthermore, clustering is widely used techniques to analyze the data within the purview of data mining and thus it became as a motivational impetus for the authors to survey the existing literature on this topic rigorously and have consequently identified various key parameters so that concomitant improvement can be possible while selecting a best fit clustering algorithm pertaining to a specific problem domain. Furthermore, clustering, classification and association rule mining are akin and indispensable to data mining and owing to these authors have also included interrelation and intertwining among these terms so that this work will presage chunk of help for the researchers working in this field. The present study also envisages and manifests the challenges associated with the clustering algorithms for two‐ and high‐dimensional databases in a flamboyant fashion. Over and above, this work identifies key parametric attributes to assess the clustering algorithms which in turn benevolent the existing work and paves the way for profound future research in this realm. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Classification Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Big Data Mining

No abstract

Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to COVID-19 pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during lockdown period and last two years 2018 and 2019 has been presented. Results: From the experimental work, it has been observed that the pollutants Ozone and Toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are Ozone, NH3, NO2, and PM10. Conclusions: The novel corona virus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control Ozone pollution as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

No abstract

No abstract