Discovering Conditional Functional Dependencies
0
Discovering
Conditional Functional Dependencies
Abstract
Due to their ability to detect
clusters with irregular boundaries, minimum spanning tree-based clustering
algorithms have been widely used in practice. However, in such clustering
algorithms, the search for nearest neighbor in the construction of minimum
spanning trees is the main source of computation and the standard solutions
take O(N2) time. In this paper, we present a fast minimum spanning
tree-inspired clustering algorithm, which, by using an efficient implementation
of the cut and the cycle property of the minimum spanning trees, can have much
better performance than O(N2).
Existing
System
Some classical algorithms rely
on either the idea of grouping the data points around some “centers” or the
idea of separating the data points using some regular geometric curves such as
hyper planes. As a result, they generally do not work well when the boundaries
of the clusters are irregular.
Proposed
system
Sufficient empirical evidences
have shown that a minimum spanning tree representation is quite invariant to
the detailed geometric changes in clusters’ boundaries. Therefore, the shape of
a cluster has little impact on the performance of minimum spanning tree
(MST)-based clustering algorithms, which allows us to overcome many of the
problems faced by the classical clustering algorithms.
Hardware Configuration:-
v RAM : 256
MB(min)
v Hard Disk : 20 GB
v Floppy Drive : 1.44 MB
v Key Board : Standard Windows
Keyboard
v Mouse : Two
or Three Button Mouse
v Monitor :
SVGA
Software Configuration:-
v Operating
System : Windows XP
v Application Server
: Tomcat 5.5
v Front End : HTML, Java, Jsp
v Scripts :
JavaScript.
v Server side Script : Java Server Pages.
v Database : My SQL 5.0
v Database
Connectivity : JDBC.
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