1 research outputs found
Document Clustering using Sequential Information Bottleneck Method
This paper illustrates the Principal Direction Divisive Partitioning (PDDP)
algorithm and describes its drawbacks and introduces a combinatorial framework
of the Principal Direction Divisive Partitioning (PDDP) algorithm, then
describes the simplified version of the EM algorithm called the spherical
Gaussian EM (sGEM) algorithm and Information Bottleneck method (IB) is a
technique for finding accuracy, complexity and time space. The PDDP algorithm
recursively splits the data samples into two sub clusters using the hyper plane
normal to the principal direction derived from the covariance matrix, which is
the central logic of the algorithm. However, the PDDP algorithm can yield poor
results, especially when clusters are not well separated from one another. To
improve the quality of the clustering results problem, it is resolved by
reallocating new cluster membership using the IB algorithm with different
settings. IB Method gives accuracy but time consumption is more. Furthermore,
based on the theoretical background of the sGEM algorithm and sequential
Information Bottleneck method(sIB), it can be obvious to extend the framework
to cover the problem of estimating the number of clusters using the Bayesian
Information Criterion. Experimental results are given to show the effectiveness
of the proposed algorithm with comparison to the existing algorithm.Comment: IEEE Publication format, ISSN 1947 5500,
http://sites.google.com/site/ijcsis