DBCSVM: Density Based Clustering Using Support VectorMachines
Data categorization is challenging job in a current scenario. The
growth rate of a multimedia data are increase day to day in an
internet technology. For the better retrieval and efficient
searching of a data, a process required for grouping the data.
However, data mining can find out helpful implicit information
in large databases. To detect the implicit useful information
from large databases various data mining techniques are use.
Data clustering is an important data mining technique for
grouping data sets into different clusters and each cluster
having same properties of data. In this paper we have taken
image data sets and firstly applying the density based clustering
to grouped the images, density based clustering grouped the
images according to the nearest feature sets but not grouped
outliers, then we used an important super hyperplane classifier
support vector machine (SVM) which classify the all outlier left
from density based clustering. This method improves the
efficiency of image grouping and gives better results.
Keywords: Classification, Clustering, DBSCAN, SVM
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ABOUT THE AUTHORS
Santosh Kumar Rai
Completed B.E in Computer sciencer Science & Engi neering from TIT, Bhopal, MP in 2009. and also obtained M.TECH in Information Technology in 2012 from SOIT RGPV Bhopal India.
Nishchol Mishra
Completed B.E in Computer Science & Engineering from SATI VIDISA and also obtained M.tech in Computer Science & Engineering from SATI Vidisa.
Santosh Kumar Rai
Completed B.E in Computer sciencer Science & Engi neering from TIT, Bhopal, MP in 2009. and also obtained M.TECH in Information Technology in 2012 from SOIT RGPV Bhopal India.
Nishchol Mishra
Completed B.E in Computer Science & Engineering from SATI VIDISA and also obtained M.tech in Computer Science & Engineering from SATI Vidisa.