A parallel <i>k</i>‐means clustering algorithm based on redundance elimination and extreme points optimization employing MapReduce

Liu, Kunkun; Xiao, Jingwei; Yang, Li; Xiao, Zheng

doi:10.1002/cpe.4109

Cited by 22 publications

(18 citation statements)

References 40 publications

(39 reference statements)

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“…The clustering of sensor nodes is usually adopted in largescale networks. Cluster-based networks provide more reliability, better coverage, greater fault tolerance, and better task allocation and energy-efficiency [13][14][15][16][17]. Several clusterbased routing protocols for LLNs/WSNs have been wellstudied and proposed in the last decade in attempts to resolve the "energy-hole" problem [12].…”

Section: Cluster-based Routing Protocolsmentioning

confidence: 99%

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

Hosen

Singh

Sharma

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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Improving quality of service (QoS) of low power and lossy networks (LLNs) in internet of things (IoT) is a major challenge. Cluster-based routing technique is an effective approach to achieve this goal. This paper proposes a QoS-aware clustering-based routing (QACR) mechanism for LLNs in Fogenabled IoT which provides a clustering, a cluster head (CH) election, and a routing path selection technique. The clustering adopts the community detection algorithm that partitions the network into clusters with available nodes' connectivity. The CH election and relay node selection both are weighted by the rank of the nodes which take node's energy, received signal strength, link quality, and number of cluster members into consideration as the ranking metrics. The number of CHs in a cluster is adaptive and varied according to a cluster state to balance the energy consumption of nodes. Besides, the protocol uses the CH role handover technique during CH election that decreases the control messages for the periodic election and cluster formation in detail. An evaluation of the QACR has performed through simulations for various scenarios. The obtained results show that the QACR improves the QoS in terms of packet delivery ratio, latency, and network lifetime compared to the existing protocols.

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Section: Cluster-based Routing Protocolsmentioning

confidence: 99%

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

Hosen

Singh

Sharma

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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“…In order to benefit from the high performance of multiprocessor computer systems, many efforts have been made to develop and implement parallel pattern analysis algorithms [1][2][3][4][5][6][7][8][9][10][11]. Improvement for the k-means algorithm (IMR-KCA) proposed in [1]. IMR-KCA provides a selection model to simplify the calculations with multiple clustering centers by analyzing the flaws of vast redundancy in traditional k -means algorithms.…”

Section: Related Researchmentioning

confidence: 99%

Density based Clustering Algorithm for Distributed Datasets using Mutual k-Nearest Neighbors

Salim¹

2019

ijacsa

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Privacy and security have always been a concern that prevents the sharing of data and impedes the success of many projects. Distributed knowledge computing, if done correctly, plays a key role in solving such a problem. The main goal is to obtain valid results while ensuring the non-disclosure of data. Density-based clustering is a powerful algorithm in analyzing uncertain data that naturally occur and affect the performance of many applications like location-based services. Nowadays, a huge number of datasets have been introduced for researchers which involve high-dimensional data points with varying densities. Such datasets contain data points with highdensity regions surrounded by data points with sparse density. The existing clustering approaches handle these situations inefficiently, especially in the context of distributed data. In this paper, we design a new decomposable density-based clustering algorithm for distributed datasets (DDBC). DDBC utilizes the concept of mutual k-nearest neighbor relationship to cluster distributed datasets with different density. The proposed DDBC algorithm is capable of preserving the privacy and security of data on each site by requiring a minimal number of transmissions to other sites.

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“…K‐means clustering is a well‐known technique for performing non‐hierarchical clustering . In K‐means methods, clusters are groups of data characterized by a small distance to the cluster center. An objective function, typically the sum of the distance to a set of putative cluster centers, is optimized until the best cluster center candidates are found.…”

Section: Related Workmentioning

confidence: 99%

A grouping approach based on non‐uniform binary grid partitioning for crowd evacuation simulation

Liu

et al. 2018

Concurrency and Computation

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Summary Small social groups based on kinship or friendships are ubiquitous in human crowds. Therefore, the effect of social groups on crowd evacuations and that of crowd evacuations on social groups must be investigated. To simulate the group phenomenon when an emergency occurs, we propose an improved social force model that takes into account the social group relationship among the population, and based on our proposed model, a novel grouping algorithm predicated on non‐uniform binary grid partitioning is put forward. The approach initially maps the individuals into the plane space, and then it adopts top‐down binary grid partitioning iteratively until the divided grid contains only the individuals with relations; then, the values of the relation and density of the non‐empty grid cells are calculated, and the grids are sorted according to these values. After sorting, selecting, merging, and forming the core grids, the other grids are merged to the core grids. We have compared the algorithm with the hierarchical classification algorithm and the grid‐based algorithm. The results show that the accuracy, speed, and scalability are all advantages. We also establish a simulation platform to illustrate the proposed grouping algorithm and the improved social force model for crowd evacuation simulation.

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A parallel k‐means clustering algorithm based on redundance elimination and extreme points optimization employing MapReduce

Cited by 22 publications

References 40 publications

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

Density based Clustering Algorithm for Distributed Datasets using Mutual k-Nearest Neighbors

A grouping approach based on non‐uniform binary grid partitioning for crowd evacuation simulation

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