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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
DNA approach to solve clustering problem based on a mutual order
Rohani Binti Abu Bakar1, Junzo Watada, Witold Pedrycz
1Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-Ku, Kitakyushu-Shi 808-0135, Japan. rohani@ump.edu.my
Abstract:
Clustering is regarded as a consortium of concepts and algorithms that are aimed at revealing a structure in highly dimensional data and arriving at a collection of meaningful relationships in data and information granules. The objective of this paper is to propose a DNA computing to support the development of clustering techniques. This approach is of particular interest when dealing with huge data sets, unknown number of clusters and encountering a heterogeneous character of available data. We present a detailed algorithm and show how the essential components of the clustering technique are realized through the corresponding mechanisms of DNA computing. Numerical examples offer a detailed insight into the performance of the DNA-based clustering.
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