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عنوان پرسش: راهنمایی برای ترجمه این متن
شرح پرسش: Unlike classification, clustering
is to enable all records to compose different classes or cluster
before we know in advance how many classes the target
database has. In the circumstance of this classification,
similarity in some measure as the standard can minimize in the
same cluster and maximize among different clusters. Reference
[11] proposes two parallel algorithms which both use the
concept of exception points called outliers. An outlier is an
observation that is so different from other observations that it
can be doubted reasonably that maybe it is generated by a
different mechanism. There are a lot of applications of Outlier
detection, for example, data cleaning, network intrusion and so
on. If there is an outlier, it can be found that some individuals
or groups have behaved differently from most of the
individuals or groups of the dataset. The two algorithms are
almost linear speedup. Reference [12] mainly discusses the
effects of clusters composed of "weak" clustering members to
clustering ensemble. "Weak" clustering refers to clustering
only better than the random division. The paper uses two
methods to get the "weak" clustering: Randomly project the
cube to the one-dimensional space; cut the data space with a
random hyperplane and then use the K-means algorithm on the
sub-space data clustering and form the cluster members. The
paper selects three consensus functions to do cluster fusion of
cluster members. The experimental results show that the
clustering fusion algorithm with cluster composed of "weak"
clustering is generally better than a classic single clustering
algorithm. The paper also points out that selecting different
consensus function makes a great influence on the fusion
results. For some data sets, the result of some consensus
function is not ideal, even non-convergence.
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گروه تخصصی: علوم انسانی > زبانهای خارجی زمان ثبت سوال: چهارشنبه، 20 آذر 1392 - 14:10:08 تعداد بازدید: 185 تعداد پاسخ: 1
 
     
 
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هدی زارعی
پ، 04/09/95 - 18:07
 
     
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