Although there have been techniques to improve the accuracy of k -means clustering algorithms, many of them are applied independently. In this paper, we present a k -means clustering algorithm with Mahalanobis distance. This is a non-trivial integration of partitioning based clustering, correlation ...
When the surface is too complex to be neatly partitioned into two clearly disjoint surfaces, the use of the Mahalanobis distance metric can produce an imbalanced partitioning. Here one can use a hybrid strategy: first try a k-means clustering based on the Mahalanobis metric, and if that ...
基于马氏距离和模糊c均值聚类的抠图算法与应用 matting algorithm and application based on mahalanobis distance and the fuzzy c-means clustering algorithm 热度: 高考零距离语文答案 热度: 驾车的视觉和位置如何判断与前车距离 热度: Mahalanobis距离【马氏距离】 ...
maximized the margin with boosting to obtain distance functions for clustering [37]. Bilenko et al. integrated the pairwise constraints (must-links and cannot-links) and metric learning into a semi-supervised clustering [38]. Clustering on many data sets shows that the performance of Kmeans ...
learningforitsmerits.AMahalanobisdistancebasedfuzzyincrementalclusteringlearningalgorithmisproposed.Experimentalresultsshow thealgorithmcannotonlyeffectivelyremedythedefectinfuzzyc-meansalgorithmbutalsoincreasetrainingaccuracy. Keywords Fuzzyc·meanscluster Mahalanobisdistance ...
The means of Component 1 and Component 2 are [-2.9617,-4.9727] and [0.9539,2.0261], which are close to mu2 and mu1, respectively. Compute the Mahalanobis distance of each point in X to each component of gm. Get d2 = mahal(gm,X); Plot X by using scatter and use marker color ...
This paper presents a fuzzy support vector classifier by integrating modified fuzzy c-means clustering based on Mahalanobis distance into fuzzy support vector data description. The proposed algorithm can be used to deal with the outlier sensitivity problem in traditional multi-class classification problems...
This distance is zero if P is at the mean of its group (being the mean defined as a vector of k components correspondent to the means of the k variables) and grows as P moves away from the mean. In the case of two-class discrimination, the Mahalanobis distance of a patient P from ...
MattingalgorithmandapplicationbasedonMahalanobisdistanceandthefuzzy C鄄meansclusteringalgorithm ZHANGMin 1) ,MINLe鄄quan 1,2) ,ZHANGQun 2) ,LIUSa 3)苣 1)SchoolofMathematicsandPhysics,UniversityofScienceandTechnologyBeijing,Beijing100083,China 2)SchoolofAutomationandElectricalEngineering,UniversityofScienceandTec...
They used a k-means clustering algorithm and found that the optimal number of clusters was 10. They assumed each cluster was composed of various proportions of the following aerosol types: (1) biomass burning (2) sulfate (3) dust (4) marine. For example, Cluster 1, denoted “Sulfurous ...