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International Electrical Engineering congress (iEEcON)

作者： Bose, Indranil chen, Xi Indian Inst Management Calcutta Kolkata India Zhejiang Univ Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781479931743

Extant research has studied customer behavior in a static manner. But customer clustering can be used to identify the dynamic behavioral patterns of customers over a period of time. We develop a method for extending the standard fuzzy c-means clustering algorithm for detection of temporal changes in customer data. The study using real-life data leads to detection of appearance of new clusters and disappearance of old clusters. Using cluster validity indexes the novel method is shown to lead to formation of clusters that are better than those produced by the fuzzy c-means (FcM) algorithm.

关键词： clusters fuzzy c-means algorithm Revenue Temporal data Usage Validity index

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A NOVEL METHOD FOR SELEcTING OPTIMAL cLASSES AND OPTIMAL SAMPLES 9

A NOVEL METHOD FOR SELECTING OPTIMAL CLASSES AND OPTIMAL SAM...

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9th International FLINS conference on computational Intelligence: Foundations and Applications

作者： Kong, Ming Pei, Zheng chang, Ruiqin Luo, Bing Xihua Univ Sch Math & Comp Engn Chengdu 610039 Sichuan Peoples R China

ISBN: (纸本)9789814324694

clustering is an important unsupervised learning technique to discover the inherent structure of a given data set. In this paper, we propose a novel method to determine optimal classes and select optimal samples in data sets, the novel method is based on fuzzy c-means algorithm and the k-means algorithm. An illustrate example shows that our method is simple and valid for clustering and pattern recognition.

关键词： fuzzy c-means algorithm k-means algorithm

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Identification of the Root canal from Dental Micro-cT Records

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16th Iberoamerican congress on Pattern Recognition

作者： Szilagyi, Laszlo Dobo-Nagy, csaba Benyo, Balazs Budapest Univ Technol & Econ Dept Control Engn & Informat Technol Budapest Hungary Univ Transylvania Fac Techn & Human Sci Sapientia Hungarian Sci Tyrgu Mure Romania Semmelweis Univ Independent Sect Radiol Budapest Hungary

ISBN: (纸本)9783642250842

This paper presents a novel semi-automated image processing procedure dedicated to the identification and characterization of the dental root canal, based on high-resolution micro-cT records. After the necessary image enhancement, parallel slices are individually segmented via histogram based quick fuzzy c-means clustering. The 3D model of root canal is built up from the segmented cross sections using the reconstruction of the inner surface, and the medial line is extracted by a 3D curve skeletonization algorithm. The central line of the root canal can finally be approximated as a 3D spline curve. The proposed procedure may support the planning of several kinds of endodontic interventions.

关键词： image processing skeleton extraction micro computed tomography fuzzy c-means algorithm

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A Modified Genetic algorithm Based FcM clustering algorithm for Magnetic Resonance Image Segmentation 5th

A Modified Genetic Algorithm Based FCM Clustering Algorithm ...

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5th International conference on Frontiers in Intelligent computing - Theory and Applications (FIcTA)

作者： Das, Sunanda De, Sourav Univ Burdwan Univ Inst Technol Dept Comp Sci & Engn Burdwan 713104 W Bengal India Cooch Behar Govt Engn Coll Dept Comp Sci & Engn Cooch Behar W Bengal India

ISBN: (纸本)9789811031533;9789811031526

In this article, we have devised modified genetic algorithm (MfGA) based fuzzy c-means algorithm, which segment magnetic resonance (MR) images. In FcM, local minimum point can be easily derived for not selecting the centroids correctly. The proposed MfGA improves the population initialization and crossover parts of GA and generate the optimized class levels of the multilevel MR images. After that, the derived optimized class levels are applied as the initial input in FcM. An extensive performance comparison of the proposed method with the conventional FcM on two MR images establishes the superiority of the proposed approach.

关键词： Segmentation clustering algorithm fuzzy c-means algorithm Genetic algorithm

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Brain Lesion Detection in MRI with fuzzy and Geostatistical Models

Brain Lesion Detection in MRI with Fuzzy and Geostatistical ...

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32nd Annual International conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBc 10)

作者： Pham, Tuan D. Univ New S Wales Sch Informat Technol & Engn Canberra ACT 2600 Australia

ISBN: (纸本)9781424441242

Automated image detection of white matter changes of the brain is essentially helpful in providing a quantitative measure for studying the association of white matter lesions with other types of biomedical data. Such study allows the possibility of several medical hypothesis validations which lead to therapeutic treatment and prevention. This paper presents a new clustering-based segmentation approach for detecting white matter changes in magnetic resonance imaging with particular reference to cognitive decline in the elderly. The proposed method is formulated using the principles of fuzzy.. means algorithm and geostatistics.

关键词： MRI biomedical MRI brain brain lesion detection clustering-based segmentation cognition cognition elderly fuzzy c-means algorithm fuzzy set theory geostatistical models geriatrics image segmentation magnetic resonance imaging medical image processing statistical analysis white matter lesions

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Few-Shot Electricity Load Forecasting Based on FcM and LSTM

Few-Shot Electricity Load Forecasting Based on FCM and LSTM

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2023 IEEE Sustainable Power and Energy conference, iSPEc 2023

作者： Wu, Qiuli Wei, changfu Liu, Peng Zhang, Wei Guangxi Power Grid Equipment Monitoring and Diagnosis Engineering Technology Research Center Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi Nanning530023 China

ISBN: (纸本)9798350322699

A large load data set is frequently required as support for accurately predicting future loads, and models are used to extract the relevant user's electricity consumption behavior, energy consumption trends, and other features in numerous historical load information to capture the load time-series change patterns. When a new transformer is installed or the data storage device is damaged, resulting in a very small sample size of historical load data, it is not only difficult to reflect the time-series characteristics of load with a small amount of load data, but it is also impossible to reasonably adjust the parameters of the prediction model during the model's training process, making it difficult to build an accurate load prediction model. To address the aforementioned issues, this paper employs the fuzzy c-means algorithm (FcM) to cluster the transformer load curves in a region to generate a sample library, and then compares the small sample load curves with the samples in the sample library to identify the three most similar transformers, uses Long Short-term Memory (LSTM) as load forecasting model, trains a fully grown load forecasting model based on the sufficient historical load data of the three transformers, and drives three fully grown base models with a small amount of targeted transformer. This paper takes the minimum prediction error as the objective function, assign different weights to the prediction results of the three base models at different moments, and output the load of the target transformer by combining the prediction results of the three base models. © 2023 IEEE.

关键词： fuzzy c-means algorithm load forecasting Long Short-term Memory

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Linguistic Valued Association Rules

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Systems Science and Systems Engineering 2002年第4期11卷 409-413页

作者： LU Jian-jiang, QIAN Zuo-pingInstitute of communications Engineering, PLA University of Science & Technology, Nanjing 210016, china Institute of Communications Engineering PLA University of Science & Technology Nanjing 210016 China

Association rules discovering and prediction with data mining method are two topics in the field of information processing. In this paper, the records in database are divided into many linguistic values expressed with normal fuzzy numbers by fuzzy c-means algorithm, and a series of linguistic valued association rules are generated. Then the records in database are mapped onto the linguistic values according to largest subject principle, and the support and confidence definitions of linguistic valued association rules are also provided. The discovering and prediction methods of the linguistic valued association rules are discussed through a weather example last.

关键词： data mining fuzzy c-means algorithm linguistic valued association rules

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Application of immunological memory to the color classification of tiles

Application of immunological memory to the color classificat...

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congress on Evolutionary computation (cEc)

作者： Liu, D Zhu, XF S China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)0780378040

Based on mechanism that the vertebrate immune system remembers the antigen it has met before by retaining in the body some memory cells, an algorithm is proposed to search for the representative of the data set by generating its memory cells. The algorithm is first tested on a two-dimensional data set with three cluster centers to see if the memory cells built could really be representative. Then it is applied to a real world application, where the fuzzy c-means algorithm (FcM) is adopted to classify the tiles into different clusters according to the color similarity. The feature vectors extracted from the tile images act as the antigen and the memory cells generated are regarded as the initial cluster centers. Its performance was compared with that obtained with randomly initialized centers. By this algorithm, the number of clusters does not require to be pre-defined. The convergence speed and clustering accuracy of FcM are also improved.

关键词： pattern clustering image classification genetic algorithms image colour analysis immunological memory cells tiles color classification vertebrate immune system antigen fuzzy c-means algorithm cluster centers

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Data-driven prediction method for characteristics of voltage sag based on fuzzy time series

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INTERNATIONAL JOURNAL OF ELEcTRIcAL POWER & ENERGY SYSTEMS 2022年 134卷 107394-107394页

作者： Wang, Ying Yang, Min-Hui Zhang, Hua-Ying Wu, Xian Hu, Wen-Xi Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Shenzhen Power Supply Bur Co Ltd New Smart City Premium Power Supply Joint Lab Shenzhen 518020 Peoples R China

To inform the power utility and users, and help them reduce the huge financial losses due to voltage sag, it is important to obtain information on voltage sag events in advance. This paper proposes a method for predicting voltage sag characteristics based on fuzzy time series. First, we propose a homologous aggregation method to eliminate redundant data representing the same disturbance event and obtain the time series of voltage sag (TSOVS), which can describe the trend of the voltage sag data. Second, this paper introduces a fuzzification method for the time series of voltage sag based on the fuzzy c-means algorithm (FcMA), which transforms the time series of voltage sag into a fuzzy time series composed of interval symbols, to characterize the mapping relationship between the disturbance and voltage sag event. Furthermore, a hidden Markov model (HMM) of voltage sag is constructed to reveal the transformation relationship among elements in the fuzzy time series, considering the causal relationship between the disturbance and voltage sag event. Finally, the occurrence time and residual voltage of the voltage sag in the future were predicted based on this transformation relation. The measured voltage sags in a province in central china were used to verify the accuracy of the proposed method, prediction results with an accuracy of up to 90%.

关键词： Voltage sag fuzzy time series Homologous aggregation fuzzy c-means algorithm Hidden Markov model

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consensus reaching process using personalized modification rules in large-scale group decision-making

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INFORMATION FUSION 2024年 103卷

作者： Guo, Lun Zhan, Jianming Kou, Gang Hubei Minzu Univ Sch Math & Stat Enshi 445000 Hubei Peoples R China Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu 611130 Peoples R China

Managing complex decision-making scenarios often hinges on the effectiveness of large-scale group decision -making (LSGDM). When confronted with a significant number of decision-makers (DMs) in LSGDM, each contributing unique backgrounds and perspectives, addressing the issues of reducing dimensionality and foster-ing consensus becomes a crucial aspect of the decision-making process. This paper addresses these challenges through several innovative approaches. First, we employ clustering methods to reduce the dimensionality of DMs. We introduce a novel fuzzy c-means clustering method that takes into account both the evaluation values and ranking of alternatives. This reduction in dimensionality serves to simplify the decision complexity and enhance the coherence of decision-related information among DMs placed within the same cluster. Once the clustering phase is complete, we propose a weight solution method for DMs within each group. This method combines the consensus level with the Spearman correlation coefficient of DMs, providing an effective means to determine the weights. Additionally, we introduce a weight solution method for each group based on the average consensus level and the number of DMs it contains. In the consensus reaching process (cRP), we implement a personalized modification rule. This rule takes into consideration the evolving consensus levels and the regret psychology exhibited by different DMs at different points in time. This dynamic approach significantly reduces both the cost and time required for consensus modifications. Finally, to validate the applicability of the proposed method, we apply it to a real-life case. comprehensive qualitative and quantitative comparative analyses are conducted to evaluate the proposed method, along with a stability analysis of the parameters involved.

关键词： Large-scale group decision-making fuzzy c-means algorithm Personalized modification rule Regret theory

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