Search - high dimensional clustering

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[Other resource] clusterinquest DL : 0: 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.
Date : 2008-10-13 Size : 4.34kb User : 陈妍
[Algorithm] clusterinquest DL : 0: 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.
Date : 2025-07-09 Size : 4kb User : 陈妍
[File Format] FACERECOGNITIONBASEDONFRACTALANDGENETICALGORITHMS. DL : 0: 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.
Date : 2025-07-09 Size : 372kb User : 阳关
[Speech/Voice recognition/combine] hddc_toolbox_1.0 DL : 0: 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
Date : 2025-07-09 Size : 49kb User : tra ba huy
[Industry research] High DL : 0: 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.
Date : 2025-07-09 Size : 189kb User : tra ba huy
[Algorithm] ORCLUS DL : 0: Finding Generalized Projected Clusters in High Dimensional Spaces - ORCLUS - A subspace clustering algorithm.-Finding Generalized Projected Clusters in High Dimensional Spaces- ORCLUS- A subspace clustering algorithm.
Date : 2025-07-09 Size : 131kb User : volkanbaykan
[Algorithm] PROCLUS DL : 0: 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
Date : 2025-07-09 Size : 128kb User : volkanbaykan
[Algorithm] ENCLUS DL : 0: 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.
Date : 2025-07-09 Size : 130kb User : volkanbaykan
[Mathimatics-Numerical algorithms] adaptive_dimension_reduction DL : 0: adaptive dimension reduction for clustering high dimensional data
Date : 2025-07-09 Size : 396kb User : prasad halgaonkar
[Books] cluster_cores DL : 0: cluster cores based clustering for high dimensional data
Date : 2025-07-09 Size : 112kb User : prasad halgaonkar
[matlab] 595643603713295726 DL : 0: 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
Date : 2025-07-09 Size : 1.32mb User : wang
[Software Engineering] PCA DL : 0: 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
Date : 2025-07-09 Size : 141kb User : f0700
[AI-NN-PR] A-survey-on-subspace-clustering--pattern-based-cl DL : 0: 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
Date : 2025-07-09 Size : 1.47mb User : 蒋华荣
[Windows CE] Density-Conscious-Subspace-Clustering-for-High-Di DL : 0: Density Conscious Subspace Clustering for High-Dimensional Data
Date : 2025-07-09 Size : 2.14mb User : 蒋华荣
[Algorithm] fanyi DL : 0: 高维聚类的一些文章，对高维聚类很有帮助，尤其是初学者-Some articles of the high-dimensional clustering of high-dimensional clustering helpful
Date : 2025-07-09 Size : 2.96mb User : 程德志
[Other] paper1006 DL : 0: 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
Date : 2025-07-09 Size : 240kb User : xiaowang
[source in ebook] High-dimensional-clusteringUntitled DL : 0: High dimensional clustering-High dimensional clusteringHigh dimensional clusteringHigh dimensional clustering
Date : 2025-07-09 Size : 1kb User : 他里雾
[Industry research] Subspace-clustering DL : 0: 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.
Date : 2025-07-09 Size : 2.36mb User : 叶新
[Other] CAN-code DL : 0: 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.
Date : 2025-07-09 Size : 1.35mb User : Ye
[Special Effects] fuzzy-c-means-clustering DL : 0: 用于高维数据或者多维图像的模糊C均值聚类算法-Used for army fuzzy c-means clustering high-dimensional data
Date : 2025-07-09 Size : 4kb User : 谭建

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