Consider  high-dimensional data set  such that for every data-point there exist an informationat only part of its dimensions, and the rest is unknown . We assume that the data emerge from real-world data, i. e. , the true (unknown) points lie close to each other such that they may be group together   .
Consider a high-dimensional data set , such that for every data-point there is incomplete information. Each object in the data set represents a real entity, which models as a point in high-dimensional space . We assume that all real entities are embedded in the same space, which means they have the same dimension. We model the lack of information for a given object as affine subspace in \mathbb{R Data clustering using flats minimum distances, using the following assumptions: 1) There are m clusters. 2) Each cluster is modeled as a ball in \mathbb{R separable data. Our suggested algorithm calculates pair-wise projection of the data. We use probabilistic considerations to prove the algorithm correctness. These probabilistic results are of independent interest, as can serve to better understand the geometry of high dimensional objects .
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a
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,
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exist an informationat only part of its dimensions, and the rest is unknown
is incomplete information. Each object in the data set represents a real entity, which models as a point in high-dimensional space
R
the data emerge from real-world data, i. e. , the true (unknown) points lie close to each other such that they may be group together
all real entities are embedded in the same space, which means they have the same dimension. We model the lack of information for a given object as affine subspace in \mathbb{R
A
Data clustering using flats minimum distances
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, using the following assumptions: 1) There are m clusters. 2) Each cluster is modeled as a ball in \mathbb{R
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separable data
A
. Our suggested algorithm calculates pair-wise projection of the data. We use probabilistic considerations to prove the algorithm correctness. These probabilistic results are of independent interest, as can serve to better understand the geometry of high dimensional objects