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22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Wu Hong-Qiang Li Xiao-Yong Fang Bin-Xing Wang Yi-Ping Beijing Univ Posts & Telecommun Sch Comp Sci & Technol Minist Educ Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing Peoples R China

ISBN: (纸本)9781509044573

With the development of mobile devices, mobile cloud computing is becoming increasingly important. One of the basic questions in mobile cloud computing is how to match user demand with cloud server resources. Based on Improved fcm (IGAfcm) algorithm, this paper proposes a scheduling scheme which is provided for mobile resources to cluster continuously, so as to reduce the size of the matching requirements during the search. Moreover, Experiments have proved that matching strategy is dynamically adjusted according to the matching score and feedback training.

关键词： Mobile cloud computing resource scheduling resource clustering fcm algorithm

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Customer Churn Prediction Using Improved fcm algorithm 3

Customer Churn Prediction Using Improved FCM Algorithm

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3rd International Conference on Information Management (ICIM)

作者： Cui, Shaoying Ding, Ning Dalian Maritime Univ Transportat Management Coll Dalian Peoples R China

ISBN: (纸本)9781509063062

Data mining can provide support for bank managers to effectively analyze and predict customer churn in the era of big data. After analyzing the reasons for the bank customer churn and the defects of fcm algorithm as a data mining algorithm, a new method of calculating the effectiveness function to improve the fcm algorithm was raised. At the same time, it has been applied to predict bank customer churn. Through data mining experiments of customer information conducted on a commercial bank, it's found out the clients have been lost and will be lost. Contrast of confusion matrixes shows that the improved fcm algorithm has high accuracy, which can provide new ideas and new methods for the analysis and prediction of bank customer churn.

关键词： Bank customers churn Empirical analysis fcm algorithm Validity function

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Improved Iteration fcm algorithm for MapReduce Research 2018

Improved Iteration FCM Algorithm for MapReduce Research

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2nd International Conference on Telecommunications and Communication Engineering (ICTCE)

作者： Kiki, Mesmin J. Mbyamm Zhang Jianbiao Kouassi, Adolphe Bonzou Beijing Univ Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Univ Technol Sch Elect Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9781450365857

Aiming at the iterative characteristics of the clustering process of fuzzy C-means clustering (CLUSTERING, fcm) algorithm, an iterative MapReduce model is used for fcm. The algorithm is optimized, the map function calculates the membership degree of each sample to the cluster center, the reduce function receives the new cluster center of the middle output of the map function, and the transfer module transmits the newest cluster center to the original map task node for the new round of mapreduce job;iterative The MapReduce model adds a transfer module to the MapReduce basic model, which effectively solves the deficiencies of the basic model in dealing with the iterative problem. In the Hadoop platform, we use fcm based on the iterative MapReduce and MapReduce basic models, respectively. The algorithm is used to diagnose the transformer;the experimental results show that the diagnostic speed of fcm algorithm based on iterative mapreduce is more than 12 times of the MapReduce basic model algorithm, the misjudgment rate is lower 12 to 15 percent, and the diagnostic efficiency of fcm algorithm is improved effectively.

关键词： Cloud Environment iteration fcm algorithm MapReduce

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Research on Online Reading User Clustering Method and Application based on fcm algorithm 22

Research on Online Reading User Clustering Method and Applic...

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4th Blockchain and Internet of Things Conference (BIOTC)

作者： Hu, Xuewei Shandong Management Univ Sch Informat Engn Jinan Peoples R China

ISBN: (纸本)9781450396622

Reading is one of the important ways for people to absorb knowledge and improve their ability. With more and more extensive network information and more and more popular digital reading, people's reading media is no longer limited to paper text. According to the analysis of various surveys, it is found that online reading has a certain impact on the traditional paper book reading. In this paper, online reading users as the research object, and through offline interviews and questionnaires in the form of 362 valid data. The data is processed, the quantitative data is imported into Matlab, and fcm algorithm is used to cluster similar users according to the characteristics of users. The experimental results show that the clustering effect is the best when SSE index determines that the clustering results are four categories, and the characteristics of the four categories of readers are significantly different. This paper analyzes the characteristics of all kinds of readers, makes corresponding recommendations according to the characteristics of different readers and users, and puts forward corresponding promotion countermeasures, so as to improve users' online reading satisfaction and the use frequency of online reading platform.

关键词： fcm algorithm Digital reading Online reading User analysis

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Clustering Optimization for Triple-Frequency Combined Observations of BDS-3 Based on Improved PSO-fcm algorithm

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REMOTE SENSING 2022年第15期14卷 3713-3713页

作者： Qian, Zhaoyong Cao, Yuhua Sun, Xiaoshuang Ni, Lei Wang, Zhiyu Chen, Xiaowei Space Engn Univ Dept Space Support Beijing 101416 Peoples R China Natl Def Univ Joint Serv Coll Beijing 100858 Peoples R China

The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve the performance of navigation services. Due to the large number of combinations and different combination performances, combinatorial clustering optimization is very important, and the efficiency of manual screening is very low. Firstly, based on the basic model, the objective equations are derived. Secondly, based on the fuzzy c-means (fcm) algorithm, three improved PSO-fcm algorithms are proposed, namely the S-PSO-fcm algorithm, L-PSO-fcm algorithm, and LOG-PSO-fcm algorithm. Thirdly, according to the different combination characteristics, the two datasets whose combined coefficients sum to 0 and 1 are emphatically discussed. Finally, the effectiveness of the improved PSO-fcm algorithms is studied based on the public dataset and the measured BeiDou-3 navigation satellite system (BDS-3) data of short baseline, long baseline, and ultra-long baseline. The results show that the performance of the proposed algorithm is better than that of the fcm algorithm, especially in short baseline and long baseline cases.

关键词： triple frequency clustering optimization combination fcm algorithm improved PSO-fcm algorithm

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The Outlier Detection Method based on an Improved fcm algorithm

The Outlier Detection Method based on an Improved FCM Algori...

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2012 International Conference on Computer Science and Mathematics,Physical Education and Management（ICCMPE 2012）

作者： Deyu Tang College of Medical Information and Engineering GuangDong Pharmaceutical University GuangZhou,China Jinlin Hu College of Information and Engineering NanChang HangKong University NanChang,China Jin Yang College of Medical Information and Engineering GuangDong Pharmaceutical University GuangZhou,China

Outlier detection is an important task in a wide variety of application areas. In this paper, we use Bootstrap method for feature selection and propose a weighted fcm (BW-fcm) algorithm for outlier *** we implement this algorithm. Small clusters are considered as outliers. If the point of the data set do not belong to any clusters with the membership threshod, it maybe is an outlier. Temporary remove this point and perform BW-fcm algorithm again. Recalculate the objective function , if a noticeable change occurred in the Objective Function (OF), the point is considered as an *** experiment result indicate that our outlier method increase the detection rate and decrease the false alarm rate.

关键词： outlier bootstrap method fcm algorithm detection

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Identifying the Mastery Concepts in Linear Algebra by Using fcm-CM algorithm

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International Conference on Frontiers of Manufacturing and Design Science (ICFMD2010)

作者： Liu, Hsiang-Chuan Yu, Yen-Kuei Yih, Jeng-Ming Chen, Chin-Chun Asia Univ Dept Bioinformat & Med Informat Wufeng 41354 Taiwan Natl Taichung Univ Grad Sch Educ Measurement & Stat Taichung 40306 Taiwan Natl Taichung Univ Dept Mat Educ Taichung 40306 Taiwan Min Hwei Coll Healthcare Management Tainan Taiwan

ISBN: (纸本)9783037850046

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed to detect non-spherical structural clusters by employing Mahalanobis distance in objective function, however, both of them need to add some constrains for Mahalanobis distance. In this paper, the authors' improved Fuzzy C-Means algorithm based on common Mahalanobis distance (fcm-CM) is used to identify the mastery concepts in linear algebra, for comparing the performances with other four partition algorithms;fcm-M, GG, GK, and fcm. The result shows that fcm-CM has better performance than others.

关键词： fcm algorithm GK algorithm GG algorithm fcm-M algorithm fcm-CM algorithm

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A new feature weighted self-adaptive fcm clustering algorithm for gene expression data

A new feature weighted self-adaptive FCM clustering algorith...

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International Conference on Consumer Electronics, Communications and Networks

作者： Suli Feng Jun Tan Xianhua Zeng Rong Wei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing University of Posts and Telecommunications College of Bioniformation. Chongqing University of Posts and Telecommunications Chongqing University of Posts and Telecommunications

ISBN: (纸本)9781457714146

When dealing with gene expression data, fcm algorithm has the following defects: sensitiveness to the initial cluster centers, need to input the cluster number preliminary and doesn't consider the different contributions of attributes of gene expression data and so on. Accordingly, this paper presents a new feature weighted self-adaptive fcm algorithm handling gene expression data. First introduce a dataset pre-processing algorithms, then determine weights of all features of gene expression data set based on message-entropy, finally introduce the features' weights into the objective function of fcm algorithm. Experimental results show that the new algorithm significantly improves the effectiveness of taxonomic notes for gene expression data.

关键词： fcm algorithm Pre-processing algorithm Message entropy Feature weighted Gene expression data

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Comparison of K-means and fcm algorithms to optimize spatiotemporal pore pressure prediction of earth dams

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RESULTS IN ENGINEERING 2024年 24卷

作者： Beiranvand, Behrang Rajaee, Taher Komasi, Mehdi Univ Qom Dept Civil Engn Qom Iran Univ Ayatollah Dept Civil Engn Ozma Boroujerdi Iran

To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (fcm) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the fcm clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, fcm clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated.

关键词： Missing data Pore pressure Instrumentation Panel data K -means algorithm fcm algorithm

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Utilization of Fuzzy C-means algorithm as a novel predictive tool for estimation of interfacial tension of hydrocarbon and brine

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第15期36卷 1107-1112页

作者： Rouhibakhsh, Karim Darvish, Houman Shiraz Univ Sch Chem Petr & Gas Engn Dept Petr Engn Shiraz Iran Islamic Azad Univ Marvdasht Branch Dept Petr Engn Marvdasht Iran

The interfacial tension that exists between brine and hydrocarbon is known as one of major properties in petroleum industries because it extremely affects oil trapping in reservoirs and consequently oil recovery. Due to aforementioned reasons the importance of investigation of this parameter has been highlighted. In the present study, Fuzzy C-means (fcm) algorithm was developed to predict interfacial tension between hydrocarbon and brine as function of different parameters such as pressure, temperature, carbon number of hydrocarbon and ionic strength of brine. The obtained results of predicting algorithm expressed its low relative error and deviation from the experimental data which gathered from the literature. Also the coefficients of determination (R-2) for training and testing data were calculated 0.9508 and 0.9309 respectively. This predictive tool is simple and user friend to utilize and can be helpful for petroleum engineers to estimate interfacial tension between hydrocarbons and brine.

关键词： EOR fcm algorithm interfacial tension hydrocarbon predicting model

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