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 o...
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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.
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 objectiv...
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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. In particular, we develop a dna-based algorithm to derive decision rules of minimal length. This new approach can be of value when dealing with a large number of objects and their attributes, in which case the complexity of rough-sets-based methods is NP-hard. The proposed algorithm shows how the essential components involved in the minimization of decision rules in data processing can be realized.
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, industri...
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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. When structuring a problem with a large number of elements in an ISM process, the crossings among elements should be minimized. This computationally complex minimization is NP-complete. The proposed algorithm describes how to calculate complex relations among elements to create a hierarchically restructured digraph. This paper also presents a new approach for applying a biological method to ISM to measure the efficiency of the algorithm in calculating a large number of elements for decision making.
Assume that there exists a collection C of subsets of a finite set S, and a positive integer K <= vertical bar S vertical bar, and we need to know whether there is a subset S' subset of S with vertical bar S...
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Assume that there exists a collection C of subsets of a finite set S, and a positive integer K <= vertical bar S vertical bar, and we need to know whether there is a subset S' subset of S with vertical bar S'vertical bar <= K such that S' contains at least one element of each subset in C In other words, S' is the subset that intersects every subset in C and is called the hitting-set. In this paper, a dna-based algorithm is proposed to solve the small hitting-set problem A small hitting-set is a flitting-set with the smallest K value, i e, the hitting-set with the smallest number of elements Furthermore, another algorithm is introduced to find the number of ones from 2(n) combinations and minimum numbers of ones represents the small hitting-set since K is expected to be as small as possible The complexity of the proposed dna-based algorithm is discussed. in terms of time complexity, Volume complexity, numbers of test tube used and the longest library strand in solution space. Finally, the simulated experiment is applied to verify the correctness of our proposed dna-based algorithm, in order to solve the well-known hitting-set problem (C) 2009 Elsevier Inc. All rights reserved.
The hitting-set problem is an NP-complete problem in set theory. Assume that we have collection C of subsets of a finite set S, and a positive integer K <= vertical bar S vertical bar, and we would like to know if ...
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ISBN:
(纸本)9780769533827
The hitting-set problem is an NP-complete problem in set theory. Assume that we have collection C of subsets of a finite set S, and a positive integer K <= vertical bar S vertical bar, and we would like to know if there is a subset S' subset of S with vertical bar S'vertical bar <= K such that S' hits (contains) at least one element from each subset in C. In this paper, the dna-based algorithm is proposed to solve the hitting-set problem. Furthermore, the simulated experiment is applied to verify correction of the proposed dna-based algorithm for solving the hitting-set problem.
This paper proposes a new approach to analyzing dna-based algorithms in molecular computation. Such protocols are characterized abstractly by: encoding, tube operations and extraction. Implementation of these approach...
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This paper proposes a new approach to analyzing dna-based algorithms in molecular computation. Such protocols are characterized abstractly by: encoding, tube operations and extraction. Implementation of these approaches involves encoding in a multiset of molecules that are assembled in a tube having a number of physical attributes. The physico-chemical state of a tube can be changed by a prescribed number of elementary operations. based on realistic definitions of these elementary operations, we define complexity of a dna-based algorithm using the physico-chemical property of each operation. We show that new algorithms for Hamiltonian path are about twice as efficient as Adleman's original one and that a recent algorithm for Max-Clique provides a similar increase in efficiency. Consequences of this approach to tube complexity and dna computing are discussed. (C) 1999 Elsevier Science Ireland Ltd. AU rights reserved.
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