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Cluster analysis is a principal method in analytics domain of data mining. The algorithm used for clustering directly influences the results obtained from applying the clustering algorithm (clusters). Data clustering is done in order to identify the patterns and trends not identifiable from just looking at the data. Clustering may be supervised (if the machine training data set is available) or unsupervised...
WSN(Wireless sensor network) depicted as an emerging technology, with its potential applications in the field of territory monitoring and industrial applications. The sensors employed in the surrounding environment will monitor the physical attribute changes such as temperature, humidity, etc. and makes notice of the data being gathered and delivers the gathered information to the (BS) base station...
The Fuzzy C Strange (FCS) points clustering algorithm takes a broad view of the Enhanced K Strange points clustering algorithm to permit a point to partly fit in to various clusters thereby generating a soft categorization for a given set of elements. To achieve this, the objective function of the hard Enhanced K Strange has been drawn-out by incorporating fuzzy membership degrees in clusters into...
Genetic algorithms are randomized search and optimization techniques guided by the principles of evolution and natural genetics. The algorithm proposed in this paper uses the enhanced K Strange points clustering algorithm instead of its classical K Means counterpart. The algorithm presented in this paper successfully addresses the issues related to longer execution time and formation of inaccurate...
Prosopis cineraria, an important multipurpose tree and vital component of the otherwise fragile ecosystem of arid and semiarid regions of India. It is highly drought tolerant and sprouts profusely during the extreme dry summer months when most other trees are leafless. P. cineraria is known to exhibit comparable genetic variations at intra-specific and inter-population levels reflected through morphological...
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