An Approach to Online Fuzzy Clustering Based on the Mahalanobis Distance Measure

2020 ◽  
pp. 364-374 ◽  
Author(s):  
Zhengbing Hu ◽  
Oleksii K. Tyshchenko
Keyword(s):  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Distance Measure

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

Construct Knowledge Structure of Linear Algebra

2011 ◽  
Vol 211-212 ◽  
pp. 793-797
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih ◽  
Sue Fen Huang
Keyword(s):  
Knowledge Management ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Clustering Algorithms ◽  
Knowledge Structure ◽  
Interpretive Structural Modeling ◽  
Cognitive Characteristics ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling

2012 ◽  
pp. 499-508
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy System ◽  
Distance Measure ◽  
System Model ◽  
Rule Base ◽  
Optimization Approach ◽  
Fuzzy Data ◽  
System Models ◽  
Fuzzy System Models

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valuate Type-2 fuzzy clustering approach. In this index, all operations are fuzzy and the minimization method is fuzzy ranking with Hamming distance. The proposed Type-2 fuzzy clustering method is used for development of indirect approach to Type-2 fuzzy modeling, where the rules are extracted from clustering fuzzy numbers (Zadeh, 1965). Then, the Type-2 fuzzy system is tuned by an inference algorithm for optimization of the main parameters of Type-2 parametric system. In this case, the parameters are: Schweizer and Sklar t-Norm and s-Norm, a-cut of rule-bases, combination of FATI and FITA inference approaches, and Yager parametric defuzzification. Finally, the proposed Type-2 fuzzy system model is applied in prediction of the steel additives in steelmaking process. It is shown that, the proposed Type-2 fuzzy system model is superior in comparison with multiple regressions and Type-1 fuzzy system model, in terms of the minimization the effect of uncertainty in the rule-base fuzzy system models an error reduction.

Kernelized Mahalanobis Distance for Fuzzy Clustering

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Shan Zeng ◽  
Xiuying Wang ◽  
Xiangjun Duan ◽  
Sen Zeng ◽  
Zuyin Xiao ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Mahalanobis Distance

New Method to Assist Discrimination of Liver Diseases by Spherical SOM with Mahalanobis Distance

2012 ◽  
Vol 16 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Norie Kanzaki ◽  
◽  
Akihiro Kanagawa ◽  
Keyword(s):  
Acute Hepatitis ◽  
Mahalanobis Distance ◽  
Blood Test ◽  
Input Data ◽  
Distance Measure ◽  
Medical Data ◽  
Data Conversion ◽  
Test Results ◽  
Automated Diagnosis ◽  
Test Items

Spherical SOM, an improved version of a kind of neutral network SOM, has successfully been applied to data analysis in a variety of fields achieving effective results. However, distance measure of commercial spherical SOM is limited to the Euclidean distance and it is not suitable enough to the analysis of biased data such as blood test results. The Mahalanobis distance is said to be effective for the analysis of such medical data. Therefore it is expected that better results should be obtained if spherical SOM with Mahalanobis distance is applied to the analysis of medical data. In this paper, we take blood test items as multi-dimensional vectors and convert the input data into Mahalanobis distance with the aim of developing an automated diagnosis system by spherical SOM with Mahalanobis distance as pseudo input data. Conversion of the input data into Mahalanobis distance ensures correct evaluations of the biased data of blood test results at the same time allowing automated diagnosis based on doctors’ intuitions and experiences. We have grouped subjects of diagnosis whose features were not well discriminated by conventional Mahalanobis distance and have administered detailed discrimination by the group and obtained better discrimination rates. While in the conventional studies TP rates for the following five categories, no liverrelated problem, hepatoma (liver cancer), acute hepatitis, chronic hepatitis and liver cirrhosis, were 100%, 70%, 100%, 80% and 60% respectively, they were 96%, 80%, 71%, 86% and 91% respectively with the proposed method. Significant results were obtained overall except for acute hepatitis.

scholarly journals Segmentasi Citra Ikan Tuna dengan Mahalanobis Histogram Thresholding dan Mahalanobis Fuzzy C-Means

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Andi Baso Kaswar ◽  
Agus Zainal Arifin ◽  
Arya Yudhi Wijaya
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Accuracy Improvement ◽  
Tuna Fish ◽  
Fuzzy C Means ◽  
Membership Value ◽  
Number Of Iterations

Abstract. Fuzzy C-Means segmentation algorithm based on Mahalanobis distance can be utilized to segment tuna fish image. However, initialization of pixels membership value and clusters centroid randomly cause the segmentation process become inefficient in terms of iteration and time of computation. This paper proposes a new method for tuna fish image segmentation by Mahalanobis Histogram Thresholding (M-HT) and Mahalanobis Fuzzy C-Means (MFCM). The proposed method consists of three main phases, namely: centroid initialization, pixel clustering and accuracy improvement. The experiment carried out obtained average of iteration amount is as many as 66 iteration with average of segmentation time as many as 162.03 second. While the average of Accuracy is 98.54%, average of Missclassification Error is 1.46%. The result shows that the proposed method can improve the efficiency of segmentation method in terms of number of iterations and time of segmentation. Besides that, the proposed method can give more accurate segmentation result compared with the conventional method.Keywords: Tuna Fish Image, Segmentation, Fuzzy Clustering, Histogram Thresholding, Mahalanobis Distance. Abstrak. Algoritma segmentasi Fuzzy C-Means berbasis jarak Mahalanobis dapat digunakan untuk mensegmentasi ikan tuna. Namun, inisialisasi derajat keanggotaan piksel dan centroid klaster secara random mengakibatkan proses segmentasi menjadi tidak efisien dalam hal iterasi dan waktu komputasi. Penelitian ini mengusulkan metode baru untuk segmentasi citra ikan tuna dengan Mahalanobis Histogram Thresholding (M-HT) dan Mahalanobis Fuzzy C-Means (MFCM). Metode ini terdiri atas tiga tahap utama, yaitu: inisialisasi centroid, pengklasteran piksel dan peningkatan akurasi. Berdasarkan hasil ekseprimen, diperoleh rata-rata jumlah iterasi sebanyak 66 iterasi dengan rata-rata waktu segmentasi 162,03 detik. Rata-rata Akurasi 98,54% dengan rata-rata tingkat Missclassification Error 1,46%. Hasil yang diperoleh menunjukkan bahwa metode yang diusulkan dapat meningkatkan efisiensi metode segmentasi dalam hal jumlah iterasi dan waktu segmentasi. Selain itu, metode yang diusulkan dapat memberikan hasil segmentasi yang lebih akurat dibandingkan dengan metode konvensional.Kata Kunci: Citra Ikan Tuna, Segmentasi, Fuzzy Clustering, Histogram Thresholding, Jarak Mahalanobis.

Mahalanobis distance among the phase portraits as damage feature

2017 ◽  
Vol 17 (4) ◽  
pp. 869-887 ◽  
Author(s):  
Riya C George ◽  
Sudib K Mishra ◽  
Mohit Dwivedi
Keyword(s):  
Phase Space ◽  
Phase Portrait ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Dynamic Responses ◽  
Phase Portraits ◽  
Wheel Load ◽  
Space Topology ◽  
Bridge Model ◽  
Phase Space Topology

A number of damage-sensitive features have been proposed based on the damage-induced changes in the phase portrait, reconstructed from the measured dynamic responses of structure. Of a number of alternatives, the change in phase space topology is the most widely acclaimed for assessment of structural health. In this study, a damage feature is proposed by contrasting the damaged phase portrait with the pristine one. The contrast is expressed using the Mahalanobis distance measure, which is remarkably simpler than the change in phase space topology in algorithmic aspect. The feature is referred as Mahalanobis distance among the phase portraits. The performance of the feature is demonstrated by its sensitivity and localization of damage. The noise immunity study reveals reasonable tolerance to the measurement noise. The feature is numerically illustrated on a shear building subjected to Lorentz chaotic excitations and natural wind excitations. The feature is experimentally verified in a railway bridge model subjected to moving wheel load of a model train. The results show the effectiveness of the feature for localizing both the support damage (as in bearing) and damage in the span. Simple analytical argument is also provided to link the phase portrait distortion with the extent and localization of damage(s).

EXPRESS: Geometrical Measurement of Cultural Differences

2021 ◽  
pp. 1069031X2110184
Author(s):  
Wolfgang Messner
Keyword(s):  
Mathematical Simulation ◽  
Cultural Differences ◽  
International Business ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Weight Vector ◽  
Cultural Dimensions ◽  
The Core ◽  
Export Marketing ◽  
Geometrical Difference

Differences and similarities between countries, regions, and cultures lie at the core of international business, and they are often measured in the form of a distance index originally proposed by Kogut and Singh. Because research results using this index are ambivalent, critical observers have challenged the concept, and proposed partial remedies in the form of a standardized Euclidean or Mahalanobis distance measure. This article suggests a different avenue, construes culture as a weight vector based on Hofstede’s cultural dimensions, and specifies a geometrical difference measurement using the angle of heterogeneity between two such vectors. Its performance is assessed using a mathematical simulation and an empirical example from the field of export marketing, which considers the effect of culture on bilateral export flows.

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