منابع مشابه
High-dimensional data clustering
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 highdimensional data usually live in different low-dimensional subspaces hidden in the original space. This paper presents a family of Gaussian mixture models designed for highdimensional data which combine the ideas of subspace c...
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Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information encountered in global dimensionality reduction tec...
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Cluster analysis divides data into groups (clusters) for the purposes of summarization or improved understanding. For example, cluster analysis has been used to group related documents for browsing, to find genes and proteins that have similar functionality, or as a means of data compression. While clustering has a long history and a large number of clustering techniques have been developed in ...
متن کاملAttribute Selection for High Dimensional Data Clustering
We present a new method to select an attribute subset (with few or no loss of information) for high dimensional data clustering. Most of existing clustering algorithms loose some of their efficiency in high dimensional data sets. One possible solution is to use only a subset of the whole set of dimensions. But the number of possible dimension subsets is too large to be fully parsed. We use a he...
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ژورنال
عنوان ژورنال: APTIKOM Journal on Computer Science and Information Technologies
سال: 2018
ISSN: 2528-2425,2528-2417
DOI: 10.11591/aptikom.j.csit.82