In this paper, we propose a 3-step DNA sequence mining algorithm, called 3s-DNASM, incorporating prefix span, length and width constraints and group search optimization. The complete mining process is comprised into following vital steps: 1) applying prefix span algorithm, 2) length and width constraints, 3) Optimal mining via group search optimization (GSO). We first present the concept of prefix span, which detects the frequent DNA sequence. Based on this prefix tree, length and width constraints are applied to handle restrictions. Finally, we adopt the group search optimization (GSO) algorithm to completeness of the mining result. The experimentation is carried out using DNA sequence dataset, and the evaluation with DNA sequence dataset showed that the 3s-DNASM system is good for sequence mining. The simulation results illustrated that when min_support=4, the number of DNA sequence mined only 29 patterns by 3s-DNASM system, and in this case prefix span mined about 2168 patterns.

Kuruva Lakshmanna

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Neelu Khare

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

International Journal of Intelligent Engineering and Systems

AbstractDNA sequence mining is essential in the study of the structure and function of the DNA sequence. A few exploration works have been published in the literature concerning sequence mining in information mining task. Similarly, in our past paper, an effective sequence mining was performed on a DNA database utilizing constraint measures and group search optimization (GSO). In that study, GSO calculation was utilized to optimize the sequence extraction process from a given DNA database. However, it is apparent that, occasionally, such an arbitrary seeking system does not accompany the optimal solution in the given time. To overcome the problem, we proposed in this work multiple constraints with hybrid firefly and GSO (HFGSO) algorithm. The complete DNA sequence mining process comprised the following three modules: (i) applying prefix span algorithm; (ii) calculating the length, width, and regular expression (RE) constraints; and (iii) optimal mining via HFGSO. First, we apply the concept of prefix span, which detects the frequent DNA sequence pattern using a prefix tree. Based on this prefix tree, length, width, and RE constraints are applied to handle restrictions. Finally, we adopt the HFGSO algorithm for the completeness of the mining result. The experimentation is carried out on the standard DNA sequence dataset, and the evaluation with DNA sequence dataset and the results show that our approach is better than the existing approach.

Fulltext

Journal of Intelligent Systems

Mining DNA Sequence Patterns with Constraints Using Hybridization of Firefly and Group Search Optimization

The ultimate goal of Data Mining is to determine the hidden information which is useful in making decisions using the large databases collected by an organization. This Data Mining involves many tasks that are to be performed during the process. Mining frequent itemsets is the one of the most important tasks in case of transactional databases. These transactional databases contain the data in very large scale where the mining of these databases involves the consumption of physical memory and time in proportion to the size of the database. A frequent pattern mining algorithm is said to be efficient only if it consumes less memory and time to mine the frequent itemsets from the given large database. Having these points in mind in this thesis we proposed a system which mines frequent itemsets in an optimized way in terms of memory and time by using cloud computing as an important factor to make the process parallel and the application is provided as a service. A complete framework which uses a proven efficient algorithm called FIN algorithm. FIN algorithm works on Nodesets and POC (pre-order coding) tree. In order to evaluate the performance of the system we conduct the experiments to compare the efficiency of the same algorithm applied in a standalone manner and in cloud computing environment on a real time data set which is traffic accidents data set. The results show that the memory consumption and execution time taken for the process in the proposed system is much lesser than those of standalone system. © Published under licence by IOP Publishing Ltd.

Journal	International Journal of Intelligent Engineering and Systems
Publisher	The Intelligent Networks and Systems Society
ISSN	2185310X
Open Access	0