Tune Up Fuzzy C-Means for Big Data: Some Novel Hybrid Clustering Algorithms Based on Initial Selection and Incremental Clustering

Sơn, Lê Hoàng; Tien, Nguyen Dang

doi:10.1007/s40815-016-0260-3

Cited by 35 publications

(6 citation statements)

References 45 publications

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“…We found that the publication collection of 1,470 papers was cited 15,299 times in other published documents, and the list of the most cited papers in 10 key research areas of Industry 4.0 is shown in Table 3, along with its yearly average citation and the affiliation of the first author. There were only three papers in the top 10 having the first author's affiliations located in Vietnam, focusing on machine learning (Pham et al, 2016), deep learning (Ngo et al, 2017), and big data (Son & Tien, 2017). Bui DT was the only scholar having two papers as first author listed in Table 3, focusing on artificial neural networks (Bui, Tuan, et al, 2016), and data mining (Bui, Ho, et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…However, it is notable that only three of the top‐cited papers in Table 3 had first authors located in Vietnam. In addition, there was only one paper in Table 3 that had entirely Vietnamese‐affiliated authors (Son & Tien, 2017). This suggests that international collaborations are very important in the field of Industry 4.0 if the research is to be highly cited.…”

Section: Discussionmentioning

confidence: 99%

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Research on Industry 4.0 and on key related technologies in Vietnam: A bibliometric analysis using Scopus

Pham-Duc

Tran

et al. 2021

Learned Publishing

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Bibliometric analysis was performed to study the development of publications related to Industry 4.0 and its key technologies in Vietnam. Comparisons with data from other ASEAN countries, and with global data have been done to identify distinctive characteristics of Industry 4.0 literature from Vietnam. The collection of 1,470 retrieved papers was analysed to answer seven research questions. Our results highlighted some valuable insights of Industry 4.0 literature in Vietnam. The number of papers in Industry 4.0 in Vietnam increased rapidly in recent years, mostly focused on Computer Science, Engineering, and Mathematics. Iran, China, and South Korea were the most productive partner countries with Vietnam in Industry 4.0. Machine learning, artificial intelligence, big data, deep learning, Internet of things, neural networks, and data mining were among the most popular research themes in Industry 4.0 in Vietnam. Vietnam ranked third among 10 Southeast Asian countries, based on the number of published papers in Industry 4.0, but the gap with the two top countries was large. Compared to the global data, the annual growth rate of Industry 4.0 papers in Vietnam, and other Southeast Asian countries was lower. Findings from this work can be helpful for other scholars in establishing potential future research lines related to Industry 4.0 in Vietnam.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Research on Industry 4.0 and on key related technologies in Vietnam: A bibliometric analysis using Scopus

Pham-Duc

Tran

et al. 2021

Learned Publishing

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“…The results demonstrated the efficiency of the suggested method. Further studies will focus on accelerating computational time of this method by GPU and examining other wheat problems or exploring advanced methods [70][71][72][73][74][75][76][77][78][79][80][81][82].…”

Section: Discussionmentioning

confidence: 99%

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Jain

Kapania

et al. 2018

Symmetry

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Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

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“…L. H. Son and N. D. Tien [39] proposed a hybrid clustering algorithm using the incremental clustering and FCM that offered a better clustering accuracy with less computational time. The method was not applicable to greater dimensions.…”

Section: Motivationmentioning

confidence: 99%

MapReduce framework based big data clustering using fractional integrated sparse fuzzy C means algorithm

2020

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Tune Up Fuzzy C-Means for Big Data: Some Novel Hybrid Clustering Algorithms Based on Initial Selection and Incremental Clustering

Cited by 35 publications

References 45 publications

Research on Industry 4.0 and on key related technologies in Vietnam: A bibliometric analysis using Scopus

Research on Industry 4.0 and on key related technologies in Vietnam: A bibliometric analysis using Scopus

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

MapReduce framework based big data clustering using fractional integrated sparse fuzzy C means algorithm

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