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4 articles for “cluster and outlier analysis”
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A Study on Spatio-temporal Variation in Ambient Temperatures over Parts of Andhra Pradesh, India
Abstract: Climate change is one of the irreversible phenomena human beings ever faced in recent past. Hydro-meteorological variables provide efficient information to study the climate patterns over any region. This paper aims on narrowing down the areas which need immediate attention since high temperatures were recorded over them. This part of the study showed that hotspots analysis and cluster and outlier analysis are quintessential for policy makers to understand the higher-risk …
Published in Journal of Remote Sensing & GIS · Vol. 5, Issue 1, 2014 · pp. 1–15 Read article
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Research Survey of Data Mining with Processing in Different fields for Next Research Directions
Abstract: Data mining (DM) is a method used for mines the different category of related data in different research fields. This is a set of different techniques like classification, clustering, prediction, outlier analysis; decision making, regression etc used for gain the knowledge for intelligence computing. In this review, explain the different research papers, architecture with components of DM, processing of DM and applications of DM in various fields for next research …
Published in Journal of Computer Technology & Applications · Vol. 11, Issue 2, 2020 · pp. 5–9 Read article
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Geospatial Representation of Interdecadal Temperature Variations over Parts of Andhra Pradesh, India using Geographic Information Systems
Abstract: Climate change is one of the holistic phenomena experienced by human beings since recent decades. Keeping the view of several other hydrometerological factors, this paper emphasizes the application of geographic information systems to represent temperature variations observed over parts of Andhra Pradesh, India. Spatio-temporal representation of thermal variations was presented using cluster and outlier analysis over GIS platform. For this purpose hot spot analysis was conducted for 1951 to 1990. …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 4, Issue 2, 2014 · pp. 10–13 Read article
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Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach
Abstract: The present work is aimed to improve quality control in steel production using SEM imaging and machine learning. High-resolution SEM images of iron ore pellets, primarily composed of hematite and magnetite, are analyzed to understand their microstructural features, which significantly impact pellet performance during reduction processes. Traditional microstructure analysis is manual, time- consuming, and prone to inconsistencies. This study proposes an automated approach using K-Means Clustering, Canny Edge Detection, DBSCAN, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 7–18 Read article