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Effective and Efficient Clustering Methods for Correlated
Probabilistic Graphs
Abstract
Recently, probabilistic graphs have attracted significant interests of the
data mining community. It is observed that correlations may exist among
adjacent edges in various probabilistic graphs. As one of the basic mining
techniques, graph clustering is widely used in exploratory data analysis,
such as data compression, information retrieval, image segmentation, etc.
Graph clustering aims to divide data into clusters according to their
similarities, and a number of algorithms have been proposed for clustering
graphs, such as the pKwikCluster algorithm, spectral clustering, k-path
clustering, etc. However, little research has been performed to develop
efficient clustering algorithms for probabilistic graphs. Particularly, it
becomes more challenging to efficiently cluster probabilistic graphs when
correlations are considered. In this paper, we define the problem of
clustering correlated probabilistic graphs. To solve the challenging problem,
we propose two algorithms, namely the PEEDR and the CPGS clustering
algorithm. For each of the proposed algorithms, we develop several
pruning techniques to further improve their efficiency. We evaluate the
effectiveness and efficiency of our algorithms and pruning methods through
comprehensive experiments.

2. Existing system
Recently, probabilistic graphs have attracted significant interests of the
data mining community. It is observed that correlations may exist among
adjacent edges in various probabilistic graphs. As one of the basic mining
techniques, graph clustering is widely used in exploratory data analysis,
such as data compression, information retrieval, image segmentation, etc.
Graph clustering aims to divide data into clusters according to their
similarities, and a number of algorithms have been proposed for clustering
graphs, such as the pKwikCluster algorithm, spectral clustering, k-path
clustering, etc. However, little research has been performed to develop
efficient clustering algorithms for probabilistic graphs. Particularly, it
becomes more challenging to efficiently cluster probabilistic graphs when
correlations are considered.
Proposed system
In this paper, we define the problem of clustering correlated probabilistic
graphs. To solve the challenging problem, we propose two algorithms,
namely the PEEDR and the CPGS clustering algorithm. For each of the
proposed algorithms, we develop several pruning techniques to further
improve their efficiency. We evaluate the effectiveness and efficiency of our
algorithms and pruning methods through comprehensive experiments.
System Configuration:-
Hardware Configuration:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)

3.  Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
Software Configuration:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.