MapReduce FCM clustering set algorithm

Kiki, Mesmin J. Mbyamm; Zhang, Jianbiao; Kouassi, Bonzou A.

doi:10.1007/s10586-020-03131-0

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…In [45], Kiki et al Have implemented a method of FCM, called Parallel FCM, which performs clustering in three steps. First, it performs a Map-Reduce on the data and calculates new clusters, which it does T times.…”

Section: Related Workmentioning

confidence: 99%

Scalable and robust big data clustering with adaptive local feature weighting based on the Map-Reduce and Hadoop

Mohammadi,

Shokrollahi,

Reisi

et al. 2023

Preprint

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Fuzzy c-means (FCM) is an effective clustering algorithm, which has been successfully applied on many real-world applications. Although, FCM and its improvements have achieved considerable performance however, most of the FCM-based methods consider equal importance for all features and neglect the feature weights in the clustering processes. To deal with this issue, in this paper, two methods called Local Feature Weighting FCM method (LFWFCM) is proposed and Robust Local Feature Weighting FCM (RLFWFCM) are proposed. RLFWFCM employs a non-Euclidian and robust metric in its process to overcome the presence of noise or outliers in data. Also pure mathematical analyses are provided to show the convergence properties of these methods. Also, these methods have been applied on the Hadoop platform by mapping reduction programming method. To assess the effectiveness of these methods, several experiments are performed on both real-world and synthetic datasets. The obtained results demonstrate the effectiveness of the proposed methods in comparison with some baseline and state-of-the-art clustering methods.

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Section: Related Workmentioning

confidence: 99%

Scalable and robust big data clustering with adaptive local feature weighting based on the Map-Reduce and Hadoop

Mohammadi,

Shokrollahi,

Reisi

et al. 2023

Preprint

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“…To capture the characteristics of the crowding occupants, the FCM algorithm employs the fuzzy theory in the determination of the membership relationship between data elements in the cluster algorithm [31]. It converts the hard division of the data elements in the traditional cluster algorithm with the membership degree between different clusters into soft division [32,33].…”

Section: Fcm Cluster Algorithmmentioning

confidence: 99%

Optimal exit choice during highway tunnel evacuations based on the fire locations

Wang

Liu

et al. 2021

PLoS ONE

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In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a strategy to obtain the optimal exit choice based on fire location during highway tunnel evacuations. In our strategy, first, the vehicle distributions and locations of evacuating occupants are determined in the traffic simulation program VISSIM. The evacuation simulation software BuildingEXODUS is employed to obtain the corresponding parameters of the evacuation process and analyze the impacts of different fire locations on the evacuation time. During the analysis, the optimal productivity statistics (OPS) is selected as the evaluation index. Then, the feature points of the crowding occupants are captured by the fuzzy c-means (FCM) cluster algorithm. Next, based on the feature points, the relationship between the location of the fire and boundary of the optimal exit choice under the optimal OPS is obtained through the polynomial regression model. It is found that the R-squared(R2) and sum of squares for error (SSE) of the polynomial regression model, reflecting the accuracy estimation, are 98.02% and 2.79×10−4, respectively. Moreover, different fire locations impact the evacuation time of tunnel entrance and evacuation passageway. This paper shows that the location of the fire and boundary of optimal exit choice have a negative linear correlation. Taking the fire 110 m away from the evacuation passageway as an example, the OPS of our strategy can be decreased by 35.6% when compared with no strategies. Our proposed strategy could be applied to determine the location of variable evacuation signs to help evacuating occupants make optimal exit choices.

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“…Clusters are viewed as a monochrome image (called an RDI or cluster heat map), shows possible clusters in the data set by dark blocks along the diagonal. Counting of square-shaped dark colored blocks is considered the cluster tendency (Suleman Basha et al, 2019;Mbyamm Kiki et al, 2020) (the number of clusters or topics). Thus, VAT (Bezdek, 2002) is a well-suited method for determining the pre-clusters estimation in the data partitioning problem.…”

Section: Related Workmentioning

confidence: 99%

Multi-viewpoints visual models for efficient modeling and analysis of Twitter based health-care services

Mohammed

2021

IJPCC

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Purpose The purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended for tweet data models for clusters assessment. Such VM determines the clusters concerning a single viewpoint or none, which are less informative. Multi-viewpoints (MVP) were used for addressing the more informative clusters assessment of health-care tweet documents and to demonstrate visual analysis of cluster tendency. Design/methodology/approach In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. The authors demonstrated the effectiveness of proposed methods on different real-time Twitter health-care data sets in the experimental study. The authors also did a comparative analysis of proposed models with existing visual assessment tendency (VAT) and cVAT models by using cluster validity indices and computational complexities; the examples suggest that MVP VM were more informative. Findings In this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. Originality/value In this paper, the authors proposed multi-viewpoints distance metric in topic model cluster tendency for the first time and visual representation using VAT images using hybrid topic models to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.

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MapReduce FCM clustering set algorithm

Cited by 14 publications

References 13 publications

Scalable and robust big data clustering with adaptive local feature weighting based on the Map-Reduce and Hadoop

Scalable and robust big data clustering with adaptive local feature weighting based on the Map-Reduce and Hadoop

Optimal exit choice during highway tunnel evacuations based on the fire locations

Multi-viewpoints visual models for efficient modeling and analysis of Twitter based health-care services

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