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 ...
The classic K-means clustering algorithm is based on the Euclidean distance,it applies only to spherical structure clustering and in the processing of data without regard to the correlation between variables and differences in the importance of each variable.To solve the above problem,this paper prop...
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 ...
关键词模糊C均值聚类中图分类号TP301.6Mahalanobis距离增量学习文献标识码AANINCREMENTALCLUSTERINGALGORITHMBASEDoNMAHALANOBISDISTANCEZhengHongliangWangJianying(SchoolofComputerandlnfomationTechnology,LiaoningNormalUniversity,Dalian116081,Liaoning,China)。(SchoolofMathematics,LiaoningNormalUniversity,Dalian116029,Liaoning,China)...
I've done some searching through the archives, and I've found some Mahalanobis-based programs, but none that do the clustering step. I'm wondering: -if this exists, and I just couldn't find it; -if it doesn't exist, is there a reason why not - some limitation or reason I'm not...
Scalable Large-Margin Mahalanobis Distance Metric Learning For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on t... C Shen,J Kim,L Wang - 《IEEE Transactions on Neural Networks》 被引量:...
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the un- derlying distance functions... JV Davis,IS Dhillon - 《Knowledge Discovery & Data Mining》 被引量: 92发表: 2008年 A closer look at novel climates: new methods and...
Fuzzy c-Means (FCM) is also a popular clustering algorithm by the distance-based objective function methods. This paper discusse... H Ichihashi,Katsuhiro Honda,N. Tani 被引量: 119发表: 2000年 Fault Detection Using Principal Components-Based Gaussian Mixture Model for Semiconductor Manufacturing ...
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 ...
对八幅彩色数字图像进行对比实验,结果显示本算法可以自动抠图,且结果优于马氏距离算法、Grow鄄Cut算法和正则化线性回归算法的相应抠图效果.关键词摇图像抠图;马氏距离;模糊C均值聚类;填洞分类号摇TP391MattingalgorithmandapplicationbasedonMahalanobisdistanceandthefuzzyC鄄meansclusteringalgorithmZHANGMin1),MINLe鄄quan1,2...