A Method for Fish Diseases Diagnosis Based on Rough Set and FCM Clustering Algorithm

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410016638874 ◽

2016 ◽

Vol 231 (1) ◽

pp. 98-108 ◽

Cited By ~ 11

Author(s):

Ke Li ◽

Yalei Wu ◽

Shimin Song ◽

Yi sun ◽

Jun Wang ◽

...

Keyword(s):

Feature Extraction ◽

Clustering Algorithm ◽

High Efficiency ◽

Computing Time ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Electrical Characteristics ◽

Complex Signals ◽

Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

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Improved Fuzzy FCM-LI Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.670 ◽

2013 ◽

Vol 765-767 ◽

pp. 670-673

Author(s):

Li Bo Hou

Keyword(s):

Real Time ◽

Clustering Algorithm ◽

Feature Analysis ◽

Cluster Center ◽

High Dimensional ◽

Fuzzy C Means ◽

Sample Data ◽

Fuzzy C Means Clustering ◽

Fcm Clustering ◽

Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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Web log mining based on improved FCM clustering algorithm

10.1117/12.867261 ◽

2010 ◽

Author(s):

Zhijun Wang ◽

Runjing Zhou

Keyword(s):

Clustering Algorithm ◽

Web Log Mining ◽

Web Log ◽

Fcm Clustering ◽

Log Mining

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Expansion Research on K-means Clustering Algorithm Based on Rough Set

International Journal of Advancements in Computing Technology ◽

10.4156/ijact.vol4.issue10.26 ◽

2012 ◽

Vol 4 (10) ◽

pp. 221-227 ◽

Cited By ~ 2

Author(s):

Xuexia YANG

Keyword(s):

Rough Set ◽

Clustering Algorithm

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Lip Region Segmentation with Complex Background

Visual Speech Recognition ◽

10.4018/978-1-60566-186-5.ch005 ◽

2009 ◽

pp. 150-171 ◽

Cited By ~ 2

Author(s):

Shilin Wang ◽

Alan Wee-Chung Liew ◽

Wing Hong Lau ◽

Shu Hung Leung

Keyword(s):

Clustering Algorithm ◽

Spatial Location ◽

Visual Speech ◽

Region Segmentation ◽

Penalty Term ◽

Complex Background ◽

Visual Speech Recognition ◽

Spatial Location Information ◽

Fcm Clustering ◽

Multiple Clusters

As the first step of many visual speech recognition and visual speaker authentication systems, robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down when dealing with lip images with complex and inhomogeneous background region such as mustaches and beards. In order to solve this problem, a Multi-class, Shapeguided FCM (MS-FCM) clustering algorithm is proposed in this chapter. In the proposed approach, one cluster is set for the lip region and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. With the spatial distribution of the lip cluster, a spatial penalty term considering the spatial location information is introduced and incorporated into the objective function such that pixels having similar color but located in different regions can be differentiated. Experimental results show that the proposed algorithm provides accurate lip-background partition even for the images with complex background features.

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