en-de
en-es
en-fr
en-pt
en-sl
en
en-zh
0.25
0.5
0.75
1.25
1.5
1.75
2
Fast approximate text document clustering using Compressive Sampling
Published on Oct 03, 20113720 Views
Document clustering involves repetitive scanning of a document set, therefore as the size of the set increases, the time required for the clustering task increases and may even become impossible due t
Related categories
Chapter list
Fast approximate text document clustering using Compressive Sampling00:00
Clustering in high dimensional spaces00:23
Outline: Coherence and Random projections01:25
Compressive Sampling02:54
Compressive sampling and reconstruction04:08
Compressive sampling for document vectors04:57
Sampling the feature space06:24
Spreading out the sparse data07:04
Sampling the spread data07:31
Outline: Compressive Clustering07:49
Clustering using the sample space07:49
Radial k-means10:15
Real approximation11:42
Outline: Performance12:14
Small Document Collection12:20
Radial k-means13:44
Radial k-means on DFT samples (1)14:32
Radial k-means on DFT samples (2)15:26
Outline: Clustering large scale document sets15:51
Large Document Collection15:59
Clustering on sparse data16:51
Large scale results17:29
Outline: Conclusion18:31
Conclusions18:33
Fast approximate text document clustering using Compressive Sampling19:00