Research and application of MapReduce-based MST text clustering algorithm

Yang, Kehua; He, Guoping; He, Guanghui

doi:10.1109/icist.2012.6221748

Cited by 4 publications

(2 citation statements)

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“…The improved algorithm showed faster result than the traditional algorithm. The paper used MapReduce model to design and implement the Minimum Spanning Tree (MST) clustering algorithm based on MST construction, graph construction, and feature extraction vector [134]. The proposed algorithm showed better scalability and accuracy results than MapReduce-based KM, but with less speed.…”

Section: Big Data Techniquesmentioning

confidence: 99%

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

et al. 2021

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This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.

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Section: Big Data Techniquesmentioning

confidence: 99%

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

et al. 2021

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“…For affinity propagation clustering algorithm, published in science magazine proposed by Frey and Dueck [28], Lu et al [29] proposed a distributed AP clustering algorithm based on MapReduce (DisAP), which includes three MapReduce stages and achieved high performance on both scalability and accuracy. In 2012, Yang et al [30] presented a MapReduce-based MST text clustering algorithm, which used cloud computing technology to improve the performance of the graph clustering. Yu and Dai [31] proposed a parallel fuzzy c-means algorithm based on MapReduce for improving the standard fuzzy c-means algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Weighted Possibilistic c-Means Algorithm for Clustering Incomplete Big Sensor Data

Zhang

Chen

2014

International Journal of Distributed Sensor Networks

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Possibilistic c-means clustering algorithm (PCM) has emerged as an important technique for pattern recognition and data analysis. Owning to the existence of many missing values, PCM is difficult to produce a good clustering result in real time. The paper proposes a distributed weighted possibillistic c-means clustering algorithm (DWPCM), which works in three steps. First the paper applies the partial distance strategy to PCM (PDPCM) for calculating the distance between any two objects in the incomplete data set. Further, a weighted PDPCM algorithm (WPCM) is designed to reduce the corruption of missing values by assigning low weight values to incomplete data objects. Finally, to improve the cluster speed of WPCM, the cloud computing technology is used to optimize the WPCM algorithm by designing the distributed weighted possibilistic c-means clustering algorithm (DWPCM) based on MapReduce. The experimental results demonstrate that the proposed algorithms can produce an appropriate partition efficiently for incomplete big sensor data.

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Big Data Clustering Using MapReduce Framework: A Review

Khader

Al‐Naymat

2020

Advances in Intelligent Systems and Computing

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Research and application of MapReduce-based MST text clustering algorithm

Cited by 4 publications

References 4 publications

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

A Distributed Weighted Possibilistic c-Means Algorithm for Clustering Incomplete Big Sensor Data

Big Data Clustering Using MapReduce Framework: A Review

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