Implementation of Cure Clustering Algorithm for Video Summarization and Healthcare Applications in Big Data

Majumdar, Jharna; Udandakar, Sumant; Bai, Bhagirathi

doi:10.1007/978-981-13-6001-5_46

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…K-implies grouping (KC) [18], probabilistic c-means (PCM) [19], fix clustering [20], and other bunching approaches exist. The KC algorithm, when compared to others, gives an advantage based on easy procedure, quick computation speed, and great clustering results; as a result, it is currently the most extensively used clustering algorithm [21]. Combining the RSAR and KC algorithms allows the RSAR method to generate suitable, which is a promising exploration course.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques

Devasekhar¹,

Natarajan²

2023

IJACSA

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Air pollution is a major environmental issue and machine learning techniques play an important role in analyzing and forecasting these data sets. Air quality is an outcome of the complex interaction of several factors involving the chemical reactions, meteorological parameters, and emissions from natural and anthropogenic sources. In this paper, we propose an efficient combined technique that takes the benefits of statistical techniques and machine learning techniques to predict/forecast the Air Quality and Pollution in particular regions. This work also indicates that prediction performance varies over different regions/cities in India. We used time series analysis, regression and Ada-boosting to anticipate PM 2.5 concentration levels in several locations throughout Hyderabad on an annual basis, depending on numerous atmospheric and surface parameters like wind speed, air temperature, pressure, and so on. Dataset for this investigation is taken from Kaggle and experimented with proposed method and comparison results of our experiments are then plotted.

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Section: Related Workmentioning

confidence: 99%

Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques

Devasekhar¹,

Natarajan²

2023

IJACSA

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“…Another example of an emerging algorithm in video data processing is the CURE clustering algorithm which can be used to generate information related to various diseases. Clustering Using Representatives (CURE) algorithm summarizes the big data to promptly give it a more meaningful form (Majumdar et al , 2019). With the aid of various video analytics tools/frameworks like Open CV, I-AVER, Spark, Online video analytics, Kestrel and many more video, analytics has become more powerful (Amalina et al , 2020 p. 7).…”

Section: Trending/emerging Technologies Of Data Miningmentioning

confidence: 99%

Exploring data mining: facets and emerging trends

Gul

Bano

Shah

2021

DLP

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Purpose Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data. Design/methodology/approach An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field. Findings The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences. Practical implications The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful. Originality/value The paper tries to highlight the current trends and facets of data mining.

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“…The CURE algorithm is mainly based on random sampling and partitioning. Random samples are taken from the data set and partition each partition, then included in the sub cluster, that in turn cluster in the second pass this process is repeated for all data points in the data set [8].…”

Section: Cure Clustering Algorithmmentioning

confidence: 99%

Subject Review: Text Clustering Algorithms

Ali

Kadhim

Abid

2020

IJERAT

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Clustering algorithms are taking attention in recent times, according to a huge amount of datasets and the growth of parallelized computing architectures. The goal of clustering algorithms is to divided the dataset into clusters, such that objects within the same cluster are similar to each other and differ from objects of other clusters. Clustering algorithms play an important role in information retrieval, indexing and text summarization. In this paper a brief overview of several clustering algorithms is discussed

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Implementation of Cure Clustering Algorithm for Video Summarization and Healthcare Applications in Big Data

Cited by 8 publications

References 5 publications

Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques

Prediction of Air Quality and Pollution using Statistical Methods and Machine Learning Techniques

Exploring data mining: facets and emerging trends

Subject Review: Text Clustering Algorithms

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