Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually exist in different low-dimensional subspaces hidden in the original space. A family of Gaussian mixtu...
1) High dimensional data clustering 高维数据聚类例句>> 2) high-dimensional categorical data 高维分类数据 1. Owing to the sparsity of high-dimensional data and the features of categorical data,it needs to develop special methods for high-dimensional categorical data. 鉴于高维数据的稀疏性和分类...
In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and ...
Clustering High-Dimensional Data Stream: A Survey on Subspace Clustering, Projected Clustering on Bioinformatics Applications (Advanced Science, Engineerin... Baghernia,Ali,Pavin,... - 《Advanced Science Engineering & Medicine》 被引量: 0发表: 2017年 Clustering High-Dimensional Data Stream: A Survey...
It requires only the maintenance of a low-dimensional embedding. Then, the clustering solution is found by applying the bisection method to the similarity matrix. In addition to the above, we propose an improvement to LSH that is beneficial for its use on high-dimensional data. This improvement...
RKHS reconstruction based on manifold learning for high-dimensional data Kernel trick has achieved remarkable success in various machine learning tasks, especially those with high-dimensional non-linear data. In addition, these ... G Niu,N Zhu,Z Ma,... - 《Applied Intelligence》 被引量: 0发表...
–X’:nd’,reduced-dimensiondataset–X:nd,high-dimensionaldataset–R:dd’,whichisgeneratedbyfirstsettingeach entryofthematrixtoavaluedrawnfromani.i.dN(0,1)distributionandthennormalizingthecolumnstounitlength.•EMclustering Aggregatingmultipleclusteringresults •Theprobabilitythatdatapointi...
Clustering high-dimensional data is an important but difficult task in various data mining applications. A fundamental starting point for data mining is the assumption that a data object, such as text document, can be represented as a high-dimensional feature vector. Traditional clustering algorithms...
In this paper we present Batch Fuzzy c-Mean with Volume Prototypes algorithm suitable to cluster large high-dimensional datasets with large chosen number of existing clusters. This algorithm is much faster than the original FCM. An important part of proposed algorithm is an initialization process of...
Finding clusters in a high dimensional data space is challenging because a high dimensional data space has hundreds of attributes and hundreds of data tupl... S Vijendra - 《Information Technology Journal》 被引量: 23发表: 2011年 Survey of clustering algorithms for high-dimensional data高维数据聚类...