منابع مشابه
Comparison of Subspace Projection Method with Traditional Clustering Algorithms for Clustering Electricity Consumption Data
There are many studies about using traditional clustering algorithms like K-means, SOM and Two-Step algorithms to cluster electricity consumption data for definition of representative consumption patterns or for further classification and prediction work. However, these approaches are lack of scalability with high dimensions. Nevertheless, they are widely used, because algorithms for clustering...
متن کاملSubspace Clustering
Data structure analysis is an important basis of machine learning and data science, which is now widely used in computational visualization problems, e.g. facial recognition, image classification, and motion segmentation. In this project, I would like to deal with a set of small classification problems and use methods like PCA, spectral analysis, kmanifold, etc. By exploring different methods, ...
متن کاملClustering Consistent Sparse Subspace Clustering
Subspace clustering is the problem of clustering data points into a union of lowdimensional linear/affine subspaces. It is the mathematical abstraction of many important problems in computer vision, image processing and machine learning. A line of recent work [4, 19, 24, 20] provided strong theoretical guarantee for sparse subspace clustering [4], the state-of-the-art algorithm for subspace clu...
متن کاملFuzzy Subspace Clustering
In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to trans...
متن کاملSubspace K-means clustering.
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning app...
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ژورنال
عنوان ژورنال: Journal of Algorithms & Computational Technology
سال: 2017
ISSN: 1748-3026,1748-3026
DOI: 10.1177/1748301817707321