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A survey on clustering algorithms for wireless sensor networks

COMPUTER COMMUNICATIONS 2007年第14-15期30卷 2826-2841页

作者： Abbasi, Ameer Ahmed Younis, Mohamed Al Hussan Inst Management & Comp Sci Dept Comp Dammam 31411 Saudi Arabia Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. To support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. In this paper, we present a taxonomy and general classification of published clustering schemes. We survey different clustering algorithms for WSNs;highlighting their objectives, features, complexity, etc. We also compare of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility. (C) 2007 Published by Elsevier B.V.

关键词： wireless sensor networks clustering algorithms scalability network architecture energy aware design

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A Survey of clustering algorithms for Big Data: Taxonomy and Empirical Analysis

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2014年第3期2卷 267-279页

作者： Fahad, Adil Alshatri, Najlaa Tari, Zahir Alamri, Abdullah Khalil, Ibrahim Zomaya, Albert Y. Foufou, Sebti Bouras, Abdelaziz RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic 3000 Australia Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia Qatar Univ Dept Comp Sci Coll Engn Doha 2713 Qatar Al Baha Univ Dept Comp Sci Al Baha City 65431 Saudi Arabia

clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

关键词： clustering algorithms unsupervised learning big data

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Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based clustering algorithms

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IEEE ACCESS 2021年 9卷 87918-87935页

作者： Bushra, Adil Abdu Yi, Gangman Dongguk Univ Dept Multimedia Engn Seoul 04620 South Korea

The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering algorithm of the density-based clustering technique. It provides the ability to handle outlier objects, detect clusters of different shapes, and disregard the need for prior knowledge about existing clusters in a dataset. These features along with its simplistic approach helped it become widely applicable in many areas of science. However, for all its accolades, the DBSCAN still has limitations in terms of performance, its ability to detect clusters of varying densities, and its dependence on user input parameters. Multiple DBSCAN-inspired algorithms have been subsequently proposed to alleviate these and more problems of the algorithm. In this paper, the implementation, features, strengths, and drawbacks of the DBSCAN are thoroughly examined. The successive algorithms proposed to provide improvement on the original DBSCAN are classified based on their motivations and are discussed. Experimental tests were conducted to understand and compare the changes presented by a C++ implementation of these algorithms along with the original DBSCAN algorithm. Finally, the analytical evaluation is presented based on the results found.

关键词： clustering algorithms Partitioning algorithms Data models Object recognition Classification algorithms Task analysis Spatial databases Unsupervised learning clustering DBSCAN spatial database

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Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2286页

作者： Wani, Aasim Ayaz Cornell Univ Sch Engn Ithaca NY 14850 USA

This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on fi ve primary methodologies: centroid-based, hierarchical, density-based, distribution-based, and graph-based clustering. Through the lens of recent innovations such as deep embedded clustering and spectral clustering, we analyze the strengths, limitations, and the breadth of application domains-ranging - ranging from bioinformatics to social network analysis. Notably, the survey introduces novel contributions by integrating clustering techniques with dimensionality reduction and proposing advanced ensemble methods to enhance stability and accuracy across varied data structures. This work uniquely synthesizes the latest advancements and offers new perspectives on overcoming traditional challenges like scalability and noise sensitivity, thus providing a comprehensive roadmap for future research and practical applications in data-intensive environments.

关键词： clustering algorithms Unsupervised learning Scalability and efficiency fi ciency Centroid-based clustering Hierarchical clustering Density-based clustering Distribution-based clustering clustering challenges and solutions

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A robustness metric for biological data clustering algorithms

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BMC BIOINFORMATICS 2019年第Sup15期20卷 1-8页

作者： Lu, Yuping Phillips, Charles A. Langston, Michael A. Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

Background: Cluster analysis is a core task in modern data-centric computation. Algorithmic choice is driven by factors such as data size and heterogeneity, the similarity measures employed, and the type of clusters sought. Familiarity and mere preference often play a significant role as well. Comparisons between clustering algorithms tend to focus on cluster quality. Such comparisons are complicated by the fact that algorithms often have multiple settings that can affect the clusters produced. Such a setting may represent, for example, a preset variable, a parameter of interest, or various sorts of initial assignments. A question of interest then is this: to what degree do the clusters produced vary as setting values change? Results: This work introduces a new metric, termed simply "robustness", designed to answer that question. Robustness is an easily-interpretable measure of the propensity of a clustering algorithm to maintain output coherence over a range of settings. The robustness of eleven popular clustering algorithms is evaluated over some two dozen publicly available mRNA expression microarray datasets. Given their straightforwardness and predictability, hierarchical methods generally exhibited the highest robustness on most datasets. Of the more complex strategies, the paraclique algorithm yielded consistently higher robustness than other algorithms tested, approaching and even surpassing hierarchical methods on several datasets. Other techniques exhibited mixed robustness, with no clear distinction between them. Conclusions: Robustness provides a simple and intuitive measure of the stability and predictability of a clustering algorithm. It can be a useful tool to aid both in algorithm selection and in deciding how much effort to devote to parameter tuning.

关键词： Robustness clustering algorithms Paraclique

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Spectrum Sharing in mmWave Cellular Networks Using clustering algorithms

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第3期28卷 1378-1390页

作者： Xie, Ning Ou-Yang, Le Liu, Alex X. Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Dongguan Univ Technol Coll Comp Sci & Network Secur Dongguan 523808 Peoples R China

This paper concerns the problem of spectrum sharing in mmWave cellular networks, where multiple network operators are granted access to the same spectrum resources. Prior spectrum sharing schemes for mmWave cellular networks have two limitations: high coordination overhead and high computational complexity. In this paper, we propose two spectrum sharing schemes for the mobile scenario and the stationary scenario, respectively. In the mobile scenario, we propose a Spectrum sharing scheme using clustering algorithms to find the Optimal Positions of Mobile BSes (SCA-OPM). In the stationary scenario, we propose a Spectrum sharing scheme using clustering algorithms to Select the most appropriate Subset from all BSes (SCA-SSB). Moreover, for each newly-arriving mobile terminal (MT), we propose two online spectrum sharing schemes based on our SCA-OPM scheme and SCA-SSB scheme, which effectively saves the overall overhead and complexity. Our experimental results show that the SCA-OPM scheme has the best performance, while the SCA-SSB scheme has the same performance as that of the prior scheme but with lower overhead and lower complexity. When the MT density is 200 MTs/km(2), for the sum-rate with 10(-2) bits/s/Hz, the performance gap between the SCA-OPM scheme and the remaining schemes achieves about 19 dB. Moreover, our online schemes have the same performance as those of their counterpart schemes but with lower overhead and lower complexity for the scenario of a few newly-arriving MTs.

关键词： Cellular networks clustering algorithms Complexity theory Interference Optimization Wireless communication Channel estimation Spectrum sharing unsupervised machine learning clustering coordination overhead computational complexity

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Objective regionalization of Rayleigh-wave dispersion data by clustering algorithms

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GEOPHYSICAL JOURNAL INTERNATIONAL 1997年第2期129卷 421-438页

作者： Lana, X Mills, GF Badal, J Canas, JA UNIV ZARAGOZA DEPT THEORET PHYS GEOPHYSE-50009 ZARAGOZASPAIN UNIV POLITECN CATALUNYA SCH CIVIL ENGNES-08034 BARCELONASPAIN

An objective regionalization of a restricted domain of the Earth's surface in terms of phase- and group-velocity data, without previous tectonic information, is presented. We apply our working scheme to local Rayleigh-wave velocities, which were determined from the study of teleseismic events recorded at the broad-band stations of the network of autonomously recording seismographs (NARS) installed in the Iberian Peninsula during the ILIHA project. According to the phase- and group-velocity maps computed at different wave periods, each point of the domain studied is characterized by two dispersion curves corresponding, respectively, to phase and group velocities. The procedure that we propose consists of the application of principal component analysis (PCA) to those velocities defining the local dispersion characteristics of the medium, and then an average linkage (AL) algorithm. It is worthy of mention that the PCA procedure enhances the relationship between local phase and group velocities. In this way, we obtain a classification of the domain into 10 homogeneous regions. Each homogeneous area is almost coincident, from a seismotectonic point of view, with either Neogene, Alpine or Hercynian domains. After the classification, we are able to deduce by stochastic inversion eight different shear-velocity structures for eight of the 10 homogeneous regions. All the structures deduced suggest a well-developed low-velocity channel from 80 to 180 km in depth for the whole Iberian Peninsula, but with a clearly distinct evolution of their shear velocities with depth. Some of the models reach shear velocities close to 4.75 km s(-1) at depths beneath this channel. The highest shear velocities, close to 5.0 km s(-1), are obtained at a depth of 50 km, and down to 200 km for region 3 (Neogene), and two possible low-velocity channels, down to a depth of 225 km, appear for models 7 and 8 (Hercynian domains). The existence of low-velocity channels in some models at depths of less th

关键词： clustering algorithms Iberian Peninsula Rayleigh waves regionalization

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Adaptive Asynchronous clustering algorithms for Wireless Mesh Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 2610-2627页

作者： Qiao, Cheng Brown, Kenneth N. Zhang, Fan Tian, Zhihong Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Univ Coll Cork Insight Ctr Data Analyt Dept Comp Sci Cork T12 K8AF Ireland

It is a challenge to generate an accurate machine learning model in a distributed network due to the increased concern in data privacy and high cost in gathering all raw data. This paper presents an adaptive asynchronous distributed clustering algorithm and two centralised methods for agents in wireless network to learn the global models, while the privacy is protected. Moreover, the communication cost and clustering quality can be adaptively balanced. The proposed clustering algorithms do not require the number of clusters to be pre-defined, and we propose a bounding boxes based method to fully utilize the shape information of clusters to improve the accuracy of the global model. Furthermore, we consider different knowledge levels of agents and different requirements about the global model. In experiments on randomly generated network topologies, we demonstrate that methods which do all the iterations of clustering in each cycle, and which exchange descriptions of cluster shape and density instead of just centroids and data counts, achieve higher accuracy, in significantly shorter elapsed time.

关键词： clustering algorithms Computational modeling Data models Costs Context modeling Wireless networks Temperature sensors Distributed algorithm asynchronous clustering algorithm wireless mesh network

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Source Apportionment of Particulate Matter by Application of Machine Learning clustering algorithms

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AEROSOL AND AIR QUALITY RESEARCH 2022年第3期22卷 1-13页

作者： Kumar, Vikas Sahu, Manoranjan Biswas, Pratim Indian Inst Technol Interdisciplinary Program Climate Studies Mumbai 400076 Maharashtra India Indian Inst Technol Environm Sci & Engn Dept Aerosol & Nanoparticle Technol Lab Mumbai 400076 Maharashtra India Indian Inst Technol Ctr Machine Intelligence & Data Sci Mumbai 400076 Maharashtra India Univ Miami Coll Engn Aerosol & Air Qual Res Lab Coral Gables FL 33146 USA

A source apportionment (SA) study was conducted on two PM2.5 data sets, two carbon fractions and eight temperature-resolved carbon fractions collected during Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). This study aimed to evaluate two clustering algorithms: k-means clustering (kMC) and spectral clustering (SC) as potential receptor models for source apportionment. The application of kMC produced unsatisfactory results, but the results obtained from SC demonstrated a significant correlation with the results obtained using positive matrix factorization (PMF). The clustering results obtained were associated with practical evidence available in the literature. SC identified six source factors on analyzing two carbon fractions data set and seven factors from eight temperature-resolved carbon fractions data set. The sources (source contribution in parentheses) identified are: combustion (45.9 +/- 3.66%) and secondary sulfate (11.4 +/- 1.09%), vegetative/wood burning (17.5 +/- 1.46%), diesel (10.6 +/- 0.92%) and gasoline (3.6 +/- 0.33%) vehicles, soil/crustal (2.07 +/- 0.2%), traffic (9.3 +/- 0.81%), and metal processing (8.8 +/- 0.72%). The source profiles obtained using SC also show similarity with the profiles derived using PMF. In summary, this study presented a basic framework for applying Machine Learning algorithms for SA analysis. Also, it presents SC as a potential receptor model technique for SA.

关键词： PM2.5 Source apportionment Receptor modeling Positive matrix factorization Machine learning clustering algorithms

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NEURAL COMPUTING & APPLICATIONS 2018年第3期30卷 987-1013页

作者： Polat, Kemal Abant Izzet Baysal Univ Fac Engn & Architecture Dept Elect & Elect Engn TR-14280 Bolu Turkey

In the fields of pattern recognition and machine learning, the use of data preprocessing algorithms has been increasing in recent years to achieve high classification performance. In particular, it has become inevitable to use the data preprocessing method prior to classification algorithms in classifying medical datasets with the nonlinear and imbalanced data distribution. In this study, a new data preprocessing method has been proposed for the classification of Parkinson, hepatitis, Pima Indians, single proton emission computed tomography (SPECT) heart, and thoracic surgery medical datasets with the nonlinear and imbalanced data distribution. These datasets were taken from UCI machine learning repository. The proposed data preprocessing method consists of three steps. In the first step, the cluster centers of each attribute were calculated using k-means, fuzzy c-means, and mean shift clustering algorithms in medical datasets including Parkinson, hepatitis, Pima Indians, SPECT heart, and thoracic surgery medical datasets. In the second step, the absolute differences between the data in each attribute and the cluster centers are calculated, and then, the average of these differences is calculated for each attribute. In the final step, the weighting coefficients are calculated by dividing the mean value of the difference to the cluster centers, and then, weighting is performed by multiplying the obtained weight coefficients by the attribute values in the dataset. Three different attribute weighting methods have been proposed: (1) similarity-based attribute weighting in k-means clustering, (2) similarity-based attribute weighting in fuzzy c-means clustering, and (3) similarity-based attribute weighting in mean shift clustering. In this paper, we aimed to aggregate the data in each class together with the proposed attribute weighting methods and to reduce the variance value within the class. Thus, by reducing the value of variance in each class, we have put together th

关键词： Imbalanced medical dataset classification Data preprocessing Attribute weighting clustering algorithms

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