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9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Yu, Qing Ding, Zhimin Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Network Secur Tianjin Peoples R China

ISBN: (纸本)9781509037100

In order to solve the problem that FCM algorithm is sensitive to initial clustering center, we use canopy algorithm to conduct the quick rough clustering. In the meantime, to avoid the blindness of canopy algorithm, we put forward an improved canopy-FCM algorithm based on max-min principle. In allusion to the problem that FCM algorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved canopy-FCM algorithm. Experimental result shows: improved canopy-FCM algorithm based on MapReduce has better clustering quality and running speed than the canopy-FCM and FCM algorithm based on MapReduce, and the improved canopy-FCM algorithm based on Hadoop has better speed-up ratio than canopy-FCM based on Standalone mode.

关键词： Clustering canopy algorithm Max-min Principle canopy-FCM algorithm MapReduce

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Equivalent Modeling of Photovoltaic Power Station Based on canopy-FCM Clustering algorithm

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IEEE ACCESS 2019年 7卷 102911-102920页

作者： Wu, Hongbin Zhang, Jiajia Luo, Chen Xu, Bin Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China State Grid Anhui Elect Power Res Inst Hefei 230601 Anhui Peoples R China

Grid-connected photovoltaic power generation has become an effective way to utilize solar energy. In order to accurately analyze the dynamic characteristics of a grid-connected photovoltaic power station, an equivalent modeling method based on the canopy-FCM clustering algorithm is proposed. Considering the lack of theoretical basis for the selection of the traditional clustering index, this paper deduces the transfer function of the grid-connected inverter control system based on the detailed model and proposes a new clustering index, which takes the zero-pole expression of the closed-loop transfer function as the prototype. In order to improve the fuzzy C-means clustering method, which is sensitive to the initial clustering centers and the outliers, a canopy algorithm with lower computation cost is introduced. Before fuzzy C-means clustering analysis, the canopy algorithm is used to determine the initial clustering centers and the number of clusters to improve the accuracy and efficiency of the clustering algorithm. The effectiveness of the proposed method is verified by simulation examples.

关键词： Grid-connected photovoltaic station improved fuzzy C-means clustering and equivalence canopy algorithm

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Research on Parallel Adaptive canopy-K-Means Clustering algorithm for Big Data Mining Based on Cloud Platform

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JOURNAL OF GRID COMPUTING 2020年第2期18卷 263-273页

作者： Xia, Dongliang Ning, Feifei He, Weina Pingdingshan Univ Sch Software Pingdingshan 467000 Henan Peoples R China

Firstly, this paper introduces the types of clustering algorithm, and introduces the classical K-means algorithm and canopy algorithm in detail. Then, combining the map reduce computing model and spark cloud computing framework, this paper introduces the parallel canopy-K-means algorithm after using canopy algorithm to optimize the initial value of K-means algorithm. However, because canopy algorithm needs to introduce a new distance threshold parameter T2, and the parameter needs to be set by human experience, it is difficult to determine the parameter artificially for large data, so this paper proposes a parallel adaptive canopy-K-means algorithm, which can be used in cloud computing framework to determine the distance threshold parameter T2 adaptively based on statistical method. Using the parallelism of Map-Reduce computing model, the parallel canopy-K-means algorithm is optimized by adaptive parameter estimation, which solves the problem that parameters depend on manual experience selection in canopy process. After introducing the relevant theories and derivation process of this algorithm, cloud computing experiment platform is built based on the Spark framework, and the contrast experiments were performed using the Stanford Large Network Dataset Collection (SNAP) dataset and self-built Dimension Networks dataset. The experimental results show that the proposed method is effective.

关键词： Big data mining Parallel framework Cloud platform K-means clustering algorithm canopy algorithm Spark framework

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Parallel Fuzzy C-Means Text Clustering algorithm Based on Improved canopy 10

Parallel Fuzzy C-Means Text Clustering Algorithm Based on Im...

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IEEE 10th International Conference on Information, Communication and Networks (ICICN)

作者： Luan, Lan Du, ShaoBo GuiZhou Univ Commerce Sch Comp & Informat Engn Guiyang Peoples R China

ISBN: (纸本)9781665490825

Fuzzy C-Means Clustering (FCM) algorithm is a flexible clustering algorithm, which can be used for large-scale data mining. However, FCM algorithm is sensitive to the selection of cluster centers and runs slowly when clustering large-scale text ***, a parallel fuzzy C-means clustering algorithm based on improved canopy is *** algorithm needs to specify a reasonable threshold to calculate, and the concept of weight is introduced to improve the canopy algorithm to improve the FCM clustering ***, the density value of each sample point in the data set is calculated, and the maximum value is selected as the first clustering center. In the calculation of the remaining sample points in the weight, and then select the center point of other clusters. Finally, the parallel computing is carried out through the Spark framework to improve the efficiency of the algorithm. The experimental results show that the fuzzy C-means parallel text clustering algorithm based on improved canopy improves the clustering accuracy by 10% -20% compared with FCM algorithm, and the overall performance of the algorithm has been improved.

关键词： fuzzy c-means canopy algorithm parallelization spark clustering

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A Dual Cluster-Head Energy-Efficient Routing algorithm Based on canopy Optimization and K-Means for WSN

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SENSORS 2022年第24期22卷 9731-9731页

作者： Wu, Mei Li, Zhengliang Chen, Jing Min, Qiusha Lu, Tao Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Math & Phys Wuhan 430205 Peoples R China Cent China Normal Univ Sch Educ Informat Technol Wuhan 430079 Peoples R China

Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node's residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols.

关键词： wireless sensor networks network lifetime canopy algorithm K-means algorithm energy-efficient

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Research on Product Reviews Hot Spot Discovery algorithm Based on Mapreduce

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IEEE ACCESS 2020年 8卷 111829-111836页

作者： Su, Hao Liu, Qicheng Mu, Chunxiao Yantai Univ Sch Comp Sci & Control Engn Yantai 264005 Peoples R China

In recent years, with the development of e-commerce, the scale of comment data has shown an exponential growth trend. In this paper, a product review hot spot discovery algorithm based on MapReduce-PR-HD is proposed. The algorithm uses the Vector Space Model to vectorize the text data of the reviews, and utilize the TF-IDF algorithm to calculate the position weight of the feature words, then combines the canopy algorithm and the K-Means algorithm to achieve the hot spot discovery of product reviews. At the same time, the algorithm obtain the ability to process massive data through the MapReduce framework. Experiments demonstrate that the PR-HD algorithm has high accuracy and parallel efficiency. This allows product developers to obtain more direct and effective suggestions and feedback, which allows product developers to obtain more direct and effective suggestions and feedback.

关键词： Clustering algorithms Data mining Partitioning algorithms Mathematical model Indexes Information technology Internet Product reviews hot spot discovery MapReduce canopy algorithm K-means algorithm

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An improved parallel K-means algorithm based on MapReduce

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2017年第3期9卷 275-282页

作者： Zhang, Dongbo Shou, Yanfang Xu, Jianmin South China Univ Technol Sch Civil Engn & Transportat Guangzhou Guangdong Peoples R China Guangdong Univ Sci & Technol Dept Comp Sci Dongguan Peoples R China South China Univ Technol Guangzhou Inst Modern Ind Technol Guangzhou Guangdong Peoples R China

The K-means algorithm is one of the most popular clustering algorithms. However, it is sensitive to initialised partitions and circular dataset. To address this problem, this paper introduces a CK-means clustering algorithm based on the K-means algorithm and the canopy algorithm, which uses the MapReduce programming model of Hadoop platform. The experimental results prove that the CK-means algorithm has strong advantages for processing large datasets. The theoretical analysis shows that the CK-means algorithm and the traditional algorithm are of the same order of magnitude. The experimental results on artificial data show that the improved algorithm is better than the traditional algorithm in terms of acceleration ratio, accuracy and expansion rate. An experiment on real data is performed to obtain appropriate parameters.

关键词： cloud computing MapReduce model K-means clusters canopy algorithm big data

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Efficient Cleaning Method of Low Quality Marine Data in Large Data Environment

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JOURNAL OF COASTAL RESEARCH 2018年第Spe83期000卷 679-684页

作者： Zhao, Guiyan Lu, Laijun Jilin Univ Coll Earth Sci Jilin 130026 Jilin Peoples R China Heilongjiang Bayi Agr Univ Coll Econ & Management Daqing 163319 Peoples R China

To optimize the cleaning of low marine quality data, it needs to get density value near each marine data quality sample. It measures samples gathered area to complete the optimization of marine feature data extraction. Traditional methods form the original transaction data set. It discovers the distribution rules of marine data, but it neglects to show the data samples gathering area. So there is low cleaning efficiency. Based on time series, big data environment, and improved canopy algorithm, it demonstrates low marine quality data optimal cleaning method. The method is to use time series model to identify marine data quantity. It classifies the marine features by time series. It uses high density clustering method to get the density value near each marine data quality sample. It shows gathered areas of samples. It introduces tag speed to the process of adaptive adjustment of sliding window to optimize the cleaning. Experimental simulations show that the proposed method of low marine quality data cleaning is highly efficiency. It guarantees the marine data quality of big data environment.

关键词： Big data environment low quality data cleaning method canopy algorithm

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Parallel K-Medoids Improved algorithm Based on MapReduce 6

Parallel K-Medoids Improved Algorithm Based on MapReduce

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6th International Conference on Advanced Cloud and BigData (CBD)

作者： Zhao, Yonghan Chen, Bin Li, Mengyu Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China

ISBN: (纸本)9781538680346

K-Medoids algorithm is a partition-based algorithm, which has the characteristics of simple implementation, strong robustness, and high accuracy. However, it has some disadvantages, such as strong dependence on the selection of initial center, the unknown number of classification K, high resource cost of frequent iteration of the algorithm, and poor clustering effect for mass data. In order to solve these problems, the original K-Medoids algorithm was improved by introducing the canopy algorithm and the Max-Min distance algorithm, and K points were selected as the initial center of the cluster. In the era of big data, we use the MapReduce computing framework to parallefize the algorithm. The experimental results show that: the improved clustering algorithm not only has a good speedup, but also improves the clustering accuracy and convergence, and shows a large performance advantage in dealing with large-scale data.

关键词： K-Medoids canopy algorithm Max-Min distance algorithm MapReduce

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A Succinct Distributive Big Data Clustering algorithm Based on Local-Remote Coordination

A Succinct Distributive Big Data Clustering Algorithm Based ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ma, Chao Liang, Xun Ma, Yuefeng Renmin Univ China Sch Informat Beijing Peoples R China

ISBN: (纸本)9781479986965

Mining global patterns on big data distributed in many remote locations is a challenging task since transmitting big data in different remote data servers to the central server is prohibitively expensive. In this paper, we present a succinct distributive big data clustering algorithm based on local-remote coordination (DBDC-LRC) that aims to reduce the cost of big data transmission while maintaining an acceptable overall clustering accuracy. The algorithm is divided into three phases. In the first phase, the idea of canopy algorithm is improved in the search for representative points with a clustering assumption that the decision boundary should lie in a low-density region, during which controllable thresholds are optimized. Noticing that in data mining a hyperellipsoid is more adaptable in shaping unknown data than a hypercube, we employ Mahalanobis distance as opposed to the Euclidean distance in determining the representative points in different remote data servers. Given that only a limited number of representative points, instead of all the remote data, are transmitted to the central server for clustering, the transmitting cost is reduced significantly. In the second phase, a weighted clustering method is used in mining the global patterns from the gathered information of representative points from various remote data servers. In the third phase, the mined global patterns are sent back to the original remote server and the related data are labeled with the same patterns according their representative points nearby. In this phase, Bayesian method is used to resolve the conflicts that one point is covered by several representative points in its neighborhood. Experiments show that DBDC-LRC is highly suitable for mining patterns from distributive big data, and the advantages of this approach include low cost, high accuracy, high robustness, and good expansibility.

关键词： DBDC-LRC representative points local Mahalanobis distance weighted clustering canopy algorithm Bayesian method

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