Description: cluster in quest聚类算法是基于密度和网格的聚类算法。对于大型数据库的高维数据聚类集合。-cluster in quest clustering algorithm is based on the density of the grid and clustering algorithm. For large database of high-dimensional data clustering pool. Platform: |
Size: 4445 |
Author:陈妍 |
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Description: cluster in quest聚类算法是基于密度和网格的聚类算法。对于大型数据库的高维数据聚类集合。-cluster in quest clustering algorithm is based on the density of the grid and clustering algorithm. For large database of high-dimensional data clustering pool. Platform: |
Size: 4096 |
Author:陈妍 |
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Description: 本文的题目是基于分形和遗传算法的人脸识别方法，对有限人群提出一种采用分形特征和遗传聚类的识别方法: 将图像分成很多小区域, 分别计算各个区域的分形特征, 以充分利用图像二维信息 同一个模式有多个样本, 通过遗传算法进行聚类以得到最优解实现不变性识别. 最后采用ORL 人脸图像库的一组图像对比了新方法、本征脸法和自联想神经网络方法, 结果表明该方法的识别率, 与本征脸法相似, 比自联想神经网络高.-The title of this article is based on fractal and genetic algorithms for face recognition method, a crowd of limited use of fractal characteristics and the identification of genetic clustering methods: the image is divided into many small regions, each region were calculated fractal characteristics, to take full advantage of two-dimensional image information with a model for a number of samples, through the genetic clustering algorithm in order to obtain the optimal solution to achieve invariant recognition. Finally, using ORL face image database of a group of image contrast of the new methods, eigenface law and auto-associative neural network methods, results show that the method of recognition rate, with the eigenface method is similar to auto-associative neural network than high. Platform: |
Size: 380928 |
Author:阳关 |
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Description: The High Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifiers for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.
Reference: C. Bouveyron, S. Girard and C. Schmid, High-Dimensional Data Clustering, Computational Statistics and Data Analysis, to appear, 2007-The High Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifiers for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.
Reference: C. Bouveyron, S. Girard and C. Schmid, High-Dimensional Data Clustering, Computational Statistics and Data Analysis, to appear, 2007 Platform: |
Size: 50176 |
Author:tra ba huy |
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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: 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: kfcm，为模糊核聚类算法，用于将低维的数据映射到高维进行分类，是较先进的算法-kfcm, the fuzzy kernel clustering algorithm for low-dimensional data is mapped to high-dimensional classification, is a more advanced algorithms Platform: |
Size: 1380352 |
Author:wang |
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Description: 提出一种基于主分量分析和相融性度量的快速聚类方法。通过构造主分量空间将高维数据投影到两个主成分上
进行特征提取，每一个主分量都是原始变量的线性组合-Is proposed based on Principal Component Analysis and Measure of blending fast clustering method. Principal component space by constructing a high-dimensional data onto two principal component on feature extraction, each principal component is a linear combination of original variables Platform: |
Size: 144384 |
Author:f0700 |
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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: 局部显著单元高维聚类算法High Dimensional Clustering Algorithm Based on Local Significant Units-High Dimensional Clustering Algorithm Based on Local Significant Units Platform: |
Size: 245760 |
Author:xiaowang |
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Description: 过去的几年见证了一个explo比如来源和形式。例如,数以百万计的摄像机被安装在建筑物、街道、机场、城市和世界各地。这造成了巨大的进步如何获取、压缩、存储、传输和处理大量复杂的高维数据。-he past few years have witnessed an explo- ple sources and modalities. For example,millions of cameras have been installed in buildings, streets, airports, and cities around the world. This has generated extraordinary advances on how to acquire, compress, store, transmit, and process massive amounts of complex high-dimensional data. Platform: |
Size: 2469888 |
Author:叶新 |
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Description: Clustering and Projected Clustering with Adaptive Neighbors
-We proposed a CAN clustering algorithm with adaptive neighbors, the learned similarity matrix can be directly used for clustering, without having to perform K-means or other discretization procedures.
Theoretical analysis reveals the proposed CAN clustering algorithm is connected with the K-means clustering problem, and the CAN can achieve much better clustering results than traditional K-means algorithm does.
For the high-dimensional clustering problem, we propose a Projected CAN (PCAN) algorithm, which performs clustering and dimensionality reduction simultaneously.
Theoretical analysis reveals the proposed PCAN clustering algorithm is connected with unsupervised LDA, and the PCAN can achieve better clustering or dimensionality reduction results than previous clustering algorithms or unsupervised dimensionality reduction algorithms do. Platform: |
Size: 1413120 |
Author:Ye |
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Search - high dimensional clustering