Description: Size and complexity of data repositories collaboratively created
by Web users generate a need for new processing approaches.
In this paper, we study the problem of detection of
fine-grained communities of users in social networks, which
can be defined as clustering with a large number of clusters
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File list (Check if you may need any files):
| Filename | Size | Date |
|---|
| main2.m | 1086 | 2017-10-18
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| sqdist.m | 152 | 2017-07-01
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| stdv.m | 156 | 2014-02-12
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| stream.m | 560 | 2017-07-01
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| Untitled2.m | 1609 | 2016-11-29
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| Untitled5.m | 4035 | 2017-02-04
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| usps38.mat | 171193 | 2014-02-12
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| apriori.m | 695 | 2017-07-01
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| eff.m | 583 | 2017-07-01
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| FpGrowth.m | 729 | 2017-07-01
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| INys.m | 664 | 2017-07-01
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| main.m | 15423 | 2017-07-20
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| G.txt | 32900 | 2017-12-11
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| Iter.txt | 2 | 2017-12-11
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| K.txt | 2 | 2017-12-11
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| test.txt | 18 | 2017-12-11
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| VV1.txt | 0 | 2017-12-11
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| 000b.mat | 54059 | 2014-02-15
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| 000c.mat | 13363 | 2014-02-17
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| 000d.mat | 17307 | 2014-02-17
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| CDTB01.mat | 9222 | 2014-03-11
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| CDTBDemo01.m | 273 | 2013-12-09
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| CDTBDemo02.m | 549 | 2014-02-17
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| CheckAll01.m | 2947 | 2014-02-16
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| CheckAll02.m | 2947 | 2014-02-16
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| CheckAll03.m | 2969 | 2014-02-16
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| ClustExp.m | 1486 | 2017-12-11 |
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