Fast Kernel Matrix Computation for Big Data Clustering

Tsapanos, Nikolaos; Tefas, Anastasios; Nikolaidis, Nikos; Iosifidis, Alexandros; Pitas, Ioannis

doi:10.1016/j.procs.2015.05.352

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Fast Kernel Matrix Computation was presented in [14] improve the computation speed for clustering big data. The space complexity of Fast Kernel Matrix Computation was higher.…”

Section: Related Workmentioning

confidence: 99%

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

K*¹,

Maheswari.²

2019

IJRTE

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Big data contains massive amounts of information’s that are difficult to manage, acquire, store and analyses. The clustering of data is a demanding issue in the field of big data analytics. The existing techniques developed for clustering does not provide efficient performance and also time complexity of clustering was higher. Further, minimizing dimensionality of big data was not addressed effectively. In order to overcome these limitations, a Moore Data Clustering based Bloom Hash Storage (MDC-BHS) Technique is proposed. The MDC-BHS Technique is designed with aim of reducing the dimensionality of big data with lesser time through clustering. The MDC-BHS Technique used Moore Data Clustering (MDC) Model in order to group the data in big dataset with minimum time consumption. After performing clustering process, the MDC-BHS Technique employed Bloom Hash Storage (BHS) Model in order to store clustered data with minimum space complexity. The BHS Model is a space-efficient probabilistic data structure which utilized hashing function to create hash value for clustered data. Therefore, proposed MDC-BHS Technique significantly reduces the dimensionality of larger dataset. The experimental evaluation of MDC-BHS technique is carried out on weather data with factors such as clustering time and clustering accuracy and space complexity with respect to number of data. The experimental results demonstrate that MDC-BHS Technique is able to improve the clustering accuracy and also minimizes the space complexity when compared to state-of-the-art works

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“…Fast Kernel Matrix Computation was presented in [14] improve the computation speed for clustering big data. The space complexity of Fast Kernel Matrix Computation was higher.…”

Section: Related Workmentioning

confidence: 99%

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

K*¹,

Maheswari.²

2019

IJRTE

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“…Choosing teaching resources according to the guidance of teachers can improve the efficiency of students in choosing teaching resources and promote students to use network teaching resources to learn. Many non-national dual first-class teaching institutions, students have a weak English foundation, even in the local dual first-class professional teaching institutions, but individual institutions enrollment quality is also bad, the quality of students is also declining, the quality of the rest of the student population is even worse, the overall level of students in English is not high, it is difficult to adapt to continue learning mode, which must be based on student characteristics to take English language learning mode match, which is to ensure the English classroom an important factor to ensure the quality of teaching [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithm in English Language Learning Pattern Matching under Big Data Framework

Zheng¹

2022

Computational Intelligence and Neuroscience

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The Internet era has brought new challenges and opportunities for English learning and English teaching. At the same time, basic education is fully implementing quality education and respecting students’ individual differences. The same teacher teaches the same content to the same class of students, but some students perform well, and some students perform poorly due to the influence of intellectual and nonintellectual factors. The uneven performance of students in the same class makes it very difficult for teachers to teach. In view of the current situation of university English teaching and the trend of respecting students’ individual development in the new era, this study investigates the basic concept of English language learning pattern matching, its main features, and practical application in the process of university English teaching. The clustering algorithm based on the big data framework is proposed for English language learning pattern matching, which is fault-tolerant and can quickly acquire and process the big data information in English teaching. By analyzing the characteristics of the data mining method of students’ English learning behavior, the method of clustering processing for students’ English learning data mining and the processing method of students’ English learning clustering data are explored. The method is highly adaptable and can be used for actual English language learning pattern matching, and actively explores the main path of English teaching change and innovation.

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Performing Hierarchical Clustering on Huge Volumes of Data Using Enhanced Mapreduce Technique

Devi¹,

Ramakrishnan

2020

Intelligent Computing and Innovation on Data Science

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Fast Kernel Matrix Computation for Big Data Clustering

Cited by 8 publications

References 18 publications

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

Clustering Algorithm in English Language Learning Pattern Matching under Big Data Framework

Performing Hierarchical Clustering on Huge Volumes of Data Using Enhanced Mapreduce Technique

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