Title :
Pattern Clustering With Statistical Methods Using a DNA-Based Algorithm
Author :
Kim, Ikno ; Watada, Junzo ; Pedrycz, Witold ; Wu, Jui-Yu
Author_Institution :
Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan
fDate :
6/1/2012 12:00:00 AM
Abstract :
Clustering is commonly exploited in engineering, management, and science fields with the objective of revealing structure in pattern data sets. In this article, through clustering we construct meaningful collections of information granules (clusters). Although the underlying goal is obvious, its realization is fully challenging. Given their nature, clustering is a well-known NP-complete problem. The existing algorithms commonly produce some suboptimal solutions. As a vehicle of pattern clustering, we discuss in this article how to use a DNA-based algorithm. We also discuss the details of encoding being used here with statistical methods combined with the DNA-based algorithm for pattern clustering.
Keywords :
DNA; biocomputing; computational complexity; molecular biophysics; pattern clustering; statistical analysis; DNA-based algorithm; NP-complete problem; information granules; pattern clustering; statistical methods; Clustering algorithms; DNA; Educational institutions; Euclidean distance; Pattern clustering; Standards; Statistical analysis; DNA-based algorithm; ordering method; pattern clustering; splicing operation; statistical method; Algorithms; Base Sequence; Cluster Analysis; Computer Simulation; DNA; Models, Genetic; Molecular Sequence Data; Pattern Recognition, Automated;
Journal_Title :
NanoBioscience, IEEE Transactions on
DOI :
10.1109/TNB.2012.2190618