Description: This paper presents a clustering approach
which estimates the specific subspace and the intrinsic dimension of each class. Our approach
adapts the Gaussian mixture model framework to high-dimensional data and estimates
the parameters which best fit the data. We obtain a robust clustering method called High-
Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images
in a probabilistic framework. Experiments on a recently proposed database demonstrate the
effectiveness of our clustering method for category localization. Platform: |
Size: 193536 |
Author:tra ba huy |
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Description: Adaptive Grids for Clustering Massive Data Sets - MAFIA. It is a subspace clustering algorithm.-Adaptive Grids for Clustering Massive Data Sets- MAFIA. It is a subspace clustering algorithm. Platform: |
Size: 137216 |
Author:volkanbaykan |
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Description: Clustering through Decision Tree Construction - CLTree - A subspace clustering algorithm.-Clustering through Decision Tree Construction- CLTree- A subspace clustering algorithm. Platform: |
Size: 89088 |
Author:volkanbaykan |
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Description: Fast Algorithms for Projected Clustering - PROCLUS - a traditional subspace clustering algorithm for high dimensional data-Fast Algorithms for Projected Clustering- PROCLUS- a traditional subspace clustering algorithm for high dimensional data Platform: |
Size: 131072 |
Author:volkanbaykan |
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Description: Entropy Based Subspace Clustering for Mining Data - ENCLUS - a new version of PROCLUS algorithm for clustering high dimensional data set.-Entropy Based Subspace Clustering for Mining Data- ENCLUS- a new version of PROCLUS algorithm for clustering high dimensional data set. Platform: |
Size: 133120 |
Author:volkanbaykan |
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Description: Clustering objects on subsets of attributes - COSA - a subspace clustering algorithm.-Clustering objects on subsets of attributes- COSA- a subspace clustering algorithm. Platform: |
Size: 410624 |
Author:volkanbaykan |
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Description: Clustering high-dimensional data A survey on subspace clustering, pattern-based clustering, and correlation clustering-Clustering high-dimensional data A survey on subspace clustering, pattern-based clustering, and correlation clustering Platform: |
Size: 1541120 |
Author:蒋华荣 |
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Description: 基于子空间方法的运动分割技术研究，包GPCA with spectral clustering，RANSAC
Local Subspace Affinity (LSA)，三种方法-Motion segmentation technique based on subspace method, including the GPCA with spectral clustering, RANSAC Local Subspace Affinity (LSA) Platform: |
Size: 20480 |
Author:liu |
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Description: 包括K-均值聚类算法的思想介绍，kmeans的MATLAB代码，c语言代码、c++代码。-Including the K-means clustering algorithm introduced the idea, kmeans of MATLAB code, c language code, c++ code.-Entropy Based Subspace Clustering for Mining Data- ENCLUS- a new version of PROCLUS algorithm for clustering high dimensional data set.
Platform: |
Size: 1130496 |
Author:陈老师 |
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Description: 聚类分析是数据挖掘研究领域中一个非常活跃的研究课题) 本文重点分析了高维度数据的自动子空间聚类算法
-Cluster analysis is data mining a very active field of research topic) This paper focuses on high-dimensional data subspace clustering algorithm automatically Platform: |
Size: 59392 |
Author:sdc |
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Description: ：聚类分析是数据挖掘研究领域中一个非常活跃的研究课题) 本文重点分析了高维度数据的自动子空间聚类算法-: Cluster analysis is data mining a very active field of research topic) This paper focuses on high-dimensional data subspace clustering algorithm automatically Platform: |
Size: 198656 |
Author:sdc |
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Description: 可实现局部最优近似平面数据聚类，该聚类方法是一种常用的子空间聚类方法，文章作者没有给出相应的源码，这里提供给大家。经过测试可以实现数据聚类，但是对人脸数据集extended yale B效果不理想。参考文献：Teng Zhang, Arthur Szlam, Yi Wang, et al. Hybrid linear modeling via local best-fit flats [J]. International Journal of Computer Vision, 2012, 100: 217-240.-This program can realize local best fit flats subspace clustering. Local best fit flats subspace clustering is one of the commonly used subspace clustering methods. The authors of the paper didn t present the source code and here it is given. The program is tested and it can be used to realize data clustering but its effect is not satisfying in clustering data extended yale B face . Reference: Teng Zhang, Arthur Szlam, Yi Wang, et al. Hybrid linear modeling via local best-fit flats [J]. International Journal of Computer Vision, 2012, 100: 217-240. Platform: |
Size: 2048 |
Author:宋昱 |
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Search - subspace clustering