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A novel multifactor type-2 fuzzy time series model based on improved fuzzy c-means algorithm and justifiable granularity for stock index forecasting

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Soft computing 2025年 1-14页

作者： Gong, Zengtai Feng, Jindong College of Mathematics and Statistics Northwest Normal University Gansu Lanzhou 730070 China College of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou 730030 China

fuzzy time series (FTS) models are widely used in prediction tasks due to their ability to handle uncertain and nonlinear problems effectively. However, type-1 fuzzy sets have limitations in dealing with data noise and linguistic ambiguity, particularly in multifactor situations. This study proposes a multifactor stock index forecasting model based on type-2 fuzzy sets and a variable size interval partitioning technique. Firstly, information granules are constructed with enhanced effectiveness and interpretability. This is achieved using the fuzzy c-means (FcM) clustering algorithm and the principle of justifiable granularity to partition the universe of discourse. The FcM algorithm in this model attains optimal clustering results by employing an interval type-2 fuzzy approach to adjust the fuzzy parameter m in FcM. Secondly, to more accurately capture the non-determinacy in the time series, three observations of the stock index are modeled as type-2 fuzzy sets, which results in a group of refined first-order fuzzy relationships. To evaluate the performance of this model, experiments were conducted using six benchmark datasets from the Taiwan Stock Exchange Index (TAIEX). The corresponding root mean square error (RMSE) was calculated as the evaluation criterion. compared with nine representative time series forecasting approaches, the experimental results demonstrate that our model not only guarantees effectiveness and robustness but also achieves enhanced prediction performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： First-order fuzzy relationships fuzzy c-means algorithm fuzzy time series Granular computing Justifiable granularity Stock index forecasting Type-2 fuzzy set

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fuzzy c-means algorithm BASED ON PSO AND MAHALANOBIS DISTANcE

FUZZY C-MEANS ALGORITHM BASED ON PSO AND MAHALANOBIS DISTANC...

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1st International Symposium on Intelligent Informatics (ISII 2008)

作者： Liu, Hsiang-chuan Yih, Jeng-Ming Lin, Wen-chih Liu, Tung-Sheng Asia Univ Dept Bioinformat Taichung 41354 Taiwan Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan Natl Taichung Univ Dept Math Educ Grad Inst Educ Measurement & Stat Taichung 40306 Taiwan

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (Gc) clustering algorithm were developed to detect non-spherical structural clusters. Both, of GG and GK algorithms suffer from the singularity problem of covariance matrix and the effect of initial status. In this paper, a new fuzzy c-means algorithm, based, on Particle Swarm Optimization and Mahalanobis distance without prior information (PSO-FcM-M) is proposed to improve those limitations of GG and GK algorithms. And we point out that the PSO-FcM algorithm is a special case of PSO-FcM-M algorithm. The experimental results of two real data sets show that the performance of our proposed PSO-FcM-M algorithm is better than those of the FcM, GG, GK algorithms.

关键词： fuzzy c-means algorithm Mahalanobis distance PSO-FcM algorithm PSO-FcM algorithm

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fuzzy c-means algorithm in Work condition Recognition of Oil Pipeline

Fuzzy C-Means Algorithm in Work Condition Recognition of Oil...

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4th IEEE conference on Industrial Electronics and Applications

作者： Ye Yingchun Zhang Laibin Liang Wei Wang Zhaohui China Univ Petr Res Ctr Oil & Gas Safety Engn Technol Beijing 102249 Peoples R China

ISBN: (纸本)9781424427994

In order to identify oil pipeline work conditions accurately and quickly, fuzzy c-means algorithm method is applied to this paper. For obtaining clustering standard, sixteen groups of raw data, which include each work condition, are selected from massive pressure data collected in the field. Analyzed data for convenience, each group of raw data is normalized with mean zero and high-frequency noise is eliminated from pressure signal by wavelet transform. The analyzed results on time-domain prove that statistical indexes can clearly and responsively describe pressure variation caused by changed work condition. The paper extracts time-domain statistical indexes from de-noised pressure data as characteristic indexes for fuzzy clustering. comprehensively considered efficiency and accuracy of fuzzy c-means algorithm, six time-domain parameters are regarded as the characteristic indexes. The clustering centers, which are found by fuzzy c-means algorithm with sixteen groups of samples' eigenvectors, are regarded as the standard of pattern recognition for work conditions. It is identified by calculating Euclidean norm between awaiting identification operation status and clustering center. Application results verify that field operation status of oil pipeline is recognized effectively and accurately. The accuracy rate of recognition is by 95%. Especially pipeline leakage is identified accurately.

关键词： fuzzy c-means algorithm work condition model recognition leak detection data mining

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Importance-Performance Analysis by fuzzy c-means algorithm

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EXPERT SYSTEMS WITH APPLIcATIONS 2016年 50卷 9-16页

作者： Ban, Olimpia I. Ban, Adrian I. Tuse, Delia A. Univ Oradea Dept Econ Univ 1 Oradea 410087 Romania Univ Oradea Dept Math & Informat Univ 1 Oradea 410087 Romania

Traditional Importance-Performance Analysis assumes the distribution of a given set of attributes in four sets, "Keep up the good work", "concentrate here", "Low priority" and "Possi... 详细信息

Traditional Importance-Performance Analysis assumes the distribution of a given set of attributes in four sets, "Keep up the good work", "concentrate here", "Low priority" and "Possible overkill", corresponding to the four possibilities, high-high, low-high, low-low and high-low, of the pair performance-importance. This can lead to ambiguities, contradictions or non-intuitive results, especially because the most real-world classes are fuzzy rather than crisp. The fuzzy clustering is an important tool to identify the structure in data, therefore we apply the fuzzy c-means algorithm to obtain a fuzzy partition of a set of attributes. A membership degree of every attribute to each of the sets mentioned above is determined, against to the forcing categorization in traditional Importance-Performance Analysis. The main benefit is related with the deriving of the managerial decisions which become more refined due to the fuzzy approach. In addition, the development priorities and the directions in which the effort of an economic or non-economic entity would be useless or even dangerous are identified on a rigorous basis and taking into account only the internal structure of the input data. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： Importance Performance Analysis fuzzy clustering fuzzy partition Prototype fuzzy c-means algorithm

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An analysis of content-based classification of audio signals using a fuzzy c-means algorithm

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MULTIMEDIA TOOLS AND APPLIcATIONS 2013年第1期63卷 77-92页

作者： Haque, Mohammad A. Kim, Jong-Myon Univ Ulsan Sch Elect Engn Ulsan 680749 South Korea

content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FcM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FcM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.

关键词： Audio segmentation and classification fuzzy c-means algorithm Multimedia Database retrieval

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A robust prediction method based on Kriging method and fuzzy c-means algorithm with application to a combine harvester

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STRUcTURAL AND MULTIDIScIPLINARY OPTIMIZATION 2022年第9期65卷 1-18页

作者： Shi, Maolin Liang, Zhenwei Zhang, Jian Xu, Lizhang Song, Xueguan Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Jiangsu Peoples R China Zhonghui Rubber Technol Co Ltd Wuxi 214183 Jiangsu Peoples R China Jiangsu Univ Fac Civil Engn & Mech Inst Struct Hlth Monitoring Zhenjiang 212013 Jiangsu Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

In real-world problems, engineering data often suffer from outliers due to deficiencies in measurement techniques, recording errors, or other reasons. In this work, a robust prediction method is proposed based on the Kriging method and fuzzy c-means algorithm. An outlier detection strategy is designed based on the fuzzy c-means algorithm, in which Kriging method is used to evaluate the relationship between the input and output. The membership of each training sample is calculated based on the prediction error of the Kriging model of each cluster and is used to judge whether the training sample is an outlier. The detected outliers are removed from the training samples, and then the remaining training samples are used to construct the final prediction model. The effect of the parameters of the proposed method on its performance is studied through an one-dimensional and a four-dimensional numerical problem, and ten benchmark functions are used to test its performance thoroughly. The results indicate that the proposed method produces much better performance in terms of outlier detection accuracy and prediction accuracy than the conventional outlier detection method and the Kriging method. Similar results can be found in the experiments on engineering problems. The proposed method is applied to model and analyze the operation data of the cleaning device of a combine harvester. The effect of the operation parameters of the cleaning device on the grain impurity ratio is studied, and the operation parameters of the cleaning device are optimized based on the prediction model of the proposed method. The analysis and optimization results provide a reference for the operation and control of the cleaning device of a combine harvester.

关键词： Robust prediction Outlier detection Kriging method fuzzy c-means algorithm combine harvester

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Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models

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SOFT cOMPUTING 2010年第5期14卷 495-505页

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Benyo, Zoltan Sapientia Univ Transylvania Fac Tech & Human Sci Corunca 547367 Romania Budapest Univ Technol & Econ Dept Control Engn & Informat Technol H-1117 Budapest Hungary

Suppressed fuzzy c-means (s-FcM) clustering was introduced in Fan et al. (Pattern Recogn Lett 24: 1607-1612, 2003) with the intention of combining the higher speed of hard c-means (HcM) clustering with the better classification properties of fuzzy c-means (FcM) algorithm. The authors modified the FcM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper, we clarify the view upon the optimality and the competitive behavior of s-FcM via analytical computations and numerical analysis. A quasi competitive learning rate (QLR) is introduced first, in order to quantify the effect of suppression. As the investigation of s-FcM's optimality did not provide a precise result, an alternative, optimally suppressed FcM (Os-FcM) algorithm is proposed as a hybridization of FcM and HcM. Both the suppressed and optimally suppressed FcM algorithms underwent the same analytical and numerical evaluations, their properties were analyzed using the QLR. We found the newly introduced Os-FcM algorithm quicker than s-FcM at any nontrivial suppression level. Os-FcM should also be favored because of its guaranteed optimality.

关键词： fuzzy c-means algorithm Suppressed fuzzy c-means algorithm competitive clustering Alternating optimization Learning rate

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Active contour model based on improved fuzzy c-means algorithm and adaptive functions

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cOMPUTERS & MATHEMATIcS WITH APPLIcATIONS 2019年第11期78卷 3678-3691页

作者： Jin, Ri Weng, Guirong Soochow Univ Sch Mech & Elect Engn Suzhou 215021 Peoples R China

Distance regularized level set evolution (DRLSE) model, which solves the re-initialization problem in early active contours, is a ground breaking edge-based model for image segmentation. However, it has the disadvantages of unsatisfactory robustness to initialization and noise, unidirectional movement, slow convergence and poor stability. In this paper, we propose an active contour model driven by improved fuzzy c-means algorithm (FcM) and adaptive functions. An adaptive sign function based on image clustering information not only increases stability, but also solves the problem of unidirectional movement. Furthermore, it gives our model the ability to selectively segment targets in image. An adaptive edge indicator function accelerates convergence with better function performance. To further increase stability, a novel double-well potential function and the corresponding evolution speed function are proposed. Due to the improved FcM, the proposed model is robust to initialization and noise. In addition, our model exhibits an edge-based and region-based characteristic. Experimental results have proved that the proposed model can not only effectively segment images with intensity inhomogeneity, but also show a good robustness to initialization. Moreover, it has shorter time spent and higher segmentation accuracy compared with other models. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Active contour model Level set method Image segmentation Distance regularized level set evolution fuzzy c-means algorithm Adaptive functions

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Improved fuzzy c-means algorithm based on density peak

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INTERNATIONAL JOURNAL OF MAcHINE LEARNING AND cYBERNETIcS 2020年第3期11卷 545-552页

作者： Liu, Xiang-yi Fan, Jian-cong chen, Zi-wen Shandong Univ Sci Coll Comp Sci Engn Technol Qingdao Peoples R China Shandong Univ Sci Prov Key Lab Informat Technol Wisdom Min Shandong Technol Qingdao Peoples R China Shandong Univ Sci Prov Expt Teaching Demonstrat Ctr Comp Technol Qingdao Peoples R China

fuzzy c-means (FcM) algorithm is a fuzzy clustering algorithm based on objective function compared with typical "hard clustering" such as k-means algorithm. FcM algorithm calculates the membership degree of each sample to all classes and obtain more reliable and accurate classification results. However, in the process of clustering, FcM algorithm needs to determine the number of clusters manually, and is sensitive to the initial clustering center. It is easy to generate problems such as multiple clustering iterations, slow convergence speed and local optimal solution. To address those problems, we propose to combine the FcM algorithm and DPc (clustering by fast search and find of density peaks) algorithm. First, DPc algorithm is used to automatically select the center and number of clusters, and then FcM algorithm is used to realize clustering. The comparison experiments show that the improved FcM algorithm has a faster convergence speed and higher accuracy.

关键词： fuzzy c-means algorithm Density peak clustering

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Reducing the time complexity of the fuzzy c-means algorithm

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IEEE TRANSAcTIONS ON fuzzy SYSTEMS 2002年第2期10卷 263-267页

作者： Kolen, JF Hutcheson, T Univ W Florida Inst Human & Machine Cognit Pensacola FL 32501 USA

In this paper, we present an efficient implementation of the fuzzy c-means clustering algorithm. The original algorithm alternates between estimating centers of the clusters and the fuzzy membership of the data points. The size of the membership matrix is on the order of the original data set, a prohibitive size if this technique is to be applied to very large data sets with many clusters. Our implementation eliminates the storage of this data structure by combining the two updates into a single update of the cluster centers. This change significantly affects the asymptotic runtime as the new algorithm is linear with respect to the number of clusters, while the original is quadratic. Elimination of the membership matrix also reduces the overhead associated with repeatedly accessing a large data structure. Empirical evidence is presented to quantify the savings achieved by this new method.

关键词： clustering methods complexity theory fuzzy c-means algorithm

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