TY  - JOUR
T1  - Metrics Free Techniques and Issues to Acquire Unifeatured High Density Quality Clusters
AU - Thangaraja, G. Abel AU - Tirumalai, Saravanan Venkataraman AU - Monickaraj, A. Pankaj Moses 
JO  - Journal of Engineering and Applied Sciences
VL  - 9
IS  - 7
SP  - 249
EP  - 253
PY  - 2014
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2014.249.253
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2014.249.253
KW  - Data mining
KW  -cluster quality
KW  -metrics
KW  -techniques and methods
KW  -inter clustering density
AB  - There are various metrics to measure the efficiency of performance say for memory byte, kilobyte and megabyte, for time, milli and micro second. Of the various research domains in data mining, clustering the unsupervised classification is one of unique area for research. To call a cluster with better quality, the intra clustering similarity should be minimum and inter clustering density, similarity should be maximum. In this study, few of the issues and techniques that have to be focused on to acquire unifeatured high density quality clusters are elaborated along with a statistical approach. The entire research study primarily focuses by 8 dimensions which are categorized into 4 each for techniques and methods.
ER  -