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fuzzy c-means algorithm Based on common Mahalanobis Distances

JOURNAL OF MULTIPLE-VALUED LOGIc AND SOFT cOMPUTING 2009年第5-6期15卷 581-595页

作者： Liu, Hsiang-chuan Yih, Jeng-Ming Lin, Wen-chih Wu, Der-Bang Natl Taichung Univ Grad Inst Educ Measurement & Stat Dept Math Educ Taichung 40306 Taiwan Asia Univ Dept Bioinformat Taichung 41354 Taiwan Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 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 (GG) clustering algorithm were developed to detect non-spherical structural clusters. However, GK algorithm needs added constraint of fuzzy covariance matrix, GK algorithm can only be used for the data with multivariate Gaussian distribution. A fuzzy c-means algorithm based on Mahalanobis distance (FcM-M) was proposed by our previous work to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. In this paper, A improved fuzzy c-means algorithm based on a common Mahalanobis distance (FcM-cM) is proposed The experimental results of three real data sets show that the performance of our proposed FcM-cM algorithm is better than those of the FcM, GG, GK and FcM-M algorithms.

关键词： fuzzy c-means algorithm GK-algorithm GG-algorithm FcM-M algorithm FcM-cM algorithm

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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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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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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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An enhanced fuzzy c-means algorithm for audio segmentation and classification

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MULTIMEDIA TOOLS AND APPLIcATIONS 2013年第2期63卷 485-500页

作者： Haque, Mohammad A. Kim, Jong-Myon Univ Ulsan Sch Elect Engn Ulsan 689749 South Korea

Automated audio segmentation and classification play important roles in multimedia content analysis. In this paper, we propose an enhanced approach, called the correlation intensive fuzzy c-means (cIFcM) algorithm, to audio segmentation and classification that is based on audio content analysis. While conventional methods work by considering the attributes of only the current frame or segment, the proposed cIFcM algorithm efficiently incorporates the influence of neighboring frames or segments in the audio stream. With this method, audio-cuts can be detected efficiently even when the signal contains audio effects such as fade-in, fade-out, and cross-fade. A number of audio features are analyzed in this paper to explore the differences between various types of audio data. The proposed cIFcM algorithm works by detecting the boundaries between different kinds of sounds and classifying them into clusters such as silence, speech, music, speech with music, and speech with noise. Our experimental results indicate that the proposed method outperforms the state-of-the-art FcM approach in terms of audio segmentation and classification.

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

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ON THE MATHEMATIcAL AND NUMERIcAL PROPERTIES OF THE fuzzy c-means algorithm

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fuzzy SETS AND SYSTEMS 1992年第2期49卷 181-191页

作者： SELIM, SZ KAMEL, MS UNIV WATERLOO DEPT SYST DESIGN ENGNWATERLOO N2L 3G1ONTARIOCANADA UNIV WATERLOO DEPT SYST DESIGNWATERLOO N2L 3G1ONTARIOCANADA

The `fuzzy clustering' problem is investigated. Interesting properties of the points generated in the course of applying the fuzzy c-means algorithm are revealed using the concept of reduced objective function. We investigate seven quantities that could be used for stopping the algorithm and prove relationships among them. Finally, we empirically show that these quantities converge linearly.

关键词： fuzzy c-means algorithm fuzzy cLUSTERING cONVERGENcE OF fuzzy c-means algorithm STOPPING cRITERIA FOR fuzzy c-means

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