Volume & Issue no: Volume 4, Issue 3, May - June 2015
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Title: |
ICM Compression System Depending On Feature Extraction |
Author Name: |
Dr.Alaa Kadhim F., Prof. Dr. Ghassan H. AbdulMajeed , Rasha Subhi Ali |
Abstract: |
Abstract
The goals of data compression is the task of providing space
on the hard drive, and reduce the use of bandwidth in the
transmission network and transfer files quickly. In this
paper intelligent techniques are used as ways of lossless data
compression. These methods applied using clustering
techniques. Clustering is one of the most important data
mining techniques. The proposed system presents a new
algorithm used to determine the best compression method.
This algorithm, called the ideal compression method (ICM).
In addition to ICM system there are three clustering
algorithms were used compress the databases and also we
proposed new decompression algorithm was used to recover
the original databases. The compression algorithms are
different in the method of selecting attributes (which
parameters are used as centers of clusters). These algorithms
are improved K-means, k- mean with the medium probability
and k-mean with maximum gain ratio. ICM used to
determine the best one of these three algorithms that can be
used to compress the database file. The main objective of this
research is to select optimal compression method for each
database. The ICM algorithm depends on the property of
min-max removal and also depends on a number of
conditions that used to determine the best compression
method. This method continued in removing min-max
column until remain one column. The residua column data
was used to specify best compression method. The standard
k-means algorithm suffering from several drawbacks such as
it was dealt with only numerical data types, number of
clusters needed to be specified by the used and the centers of
clusters selected randomly. The three compression
algorithms are modification algorithms to the standard kmeans
algorithm. The modification was proposed in selecting
the number of the clusters centers, specifying the number of
the clusters and in dealing with the data types. Several
experiments on different databases have been performed and
the results are compared. The results shows that the
maximum saving percentage is 98% minimum saving
percentage is 61%, maximum decompression time is around
14 minutes, minimum decompression time is 6 seconds,
maximum compression time is 17 minutes, minimum
compression time is 5 seconds, maximum compression size is
1073 kilobytes and minimum compression size is 7 kilobytes.
This research is organized as follow. Section one shows the
introduction, Section two explains major clustering
techniques, Section four shows the methodology of
compression and decompression algorithms and system
structure, Section five presents experiments and results and
section six offers the conclusion.
Keywords:compression algorithm, ICM system,
improved k-means algorithm, modified improved kmeans
algorithms, decompression algorithm. |
Cite this article: |
Dr.Alaa Kadhim F., Prof. Dr. Ghassan H. AbdulMajeed , Rasha Subhi Ali , "
ICM Compression System Depending On Feature Extraction " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 4, Issue 3, May - June 2015 , pp.
047-055 , ISSN 2278-6856.
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