M. Punithavalli
Clustering Time Series Data Stream - A Literature Survey.
Tue, 05/25/2010 - 15:01 — SomNambulAuthors: M. Punithavalli, V.Kavitha
Subjects: Information Retrieval
AbstractMining Time Series data has a tremendous growth of interest in today's world.
To provide an indication various implementations are studied and summarized to
identify the different problems in existing applications. Clustering time
series is a trouble that has applications in an extensive assortment of fields
and has recently attracted a large amount of research. Time series data are
frequently large and may contain outliers. In addition, time series are a
special type of data set where elements have a temporal ordering.An Analytical Study on Behavior of Clusters Using K Means, EM and K* Means Algorithm.
Tue, 04/13/2010 - 15:02 — SomNambulAuthors: M. Punithavalli, G. Nathiya, S. C. Punitha
Subjects: Learning
AbstractClustering is an unsupervised learning method that constitutes a cornerstone
of an intelligent data analysis process. It is used for the exploration of
inter-relationships among a collection of patterns, by organizing them into
homogeneous clusters. Clustering has been dynamically applied to a variety of
tasks in the field of Information Retrieval (IR). Clustering has become one of
the most active area of research and the development.Document Clustering using Sequential Information Bottleneck Method.
Tue, 04/13/2010 - 15:01 — SomNambulAuthors: P.J.Gayathri, S.C. Punitha, M. Punithavalli
Subjects: Information Retrieval
AbstractThis 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.
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