Pattern clustering with statistical methods using a DNA-based algorithm.

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.

A DNA-based algorithm for minimizing decision rules: a rough sets approach.

Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally demanding. To address this obstacle, the objective of this study is to investigate the use of DNA molecules and associated techniques as an optimization vehicle to support algorithms of rough sets.

Decision making with an interpretive structural modeling method using a DNA-based algorithm.

A novel method of interpretive structural modeling (ISM) using a DNA-based algorithm is proposed in this paper. ISM is commonly used when the current technology and its application to business administration, industrial and systems engineering, organizational behavior, etc., concern complicated or problematic issues, or situations among an element set of the given problem context for making decisions.