Open Access   Article Go Back

A Survey on Bio Inspired Algorithms: An Efficient Approach for Frequent Path Mining

S.Kiruthika 1 , A. Malathi2

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1445-1452, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.14451452

Online published on May 31, 2019

Copyright © S.Kiruthika, A. Malathi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: S.Kiruthika, A. Malathi, “A Survey on Bio Inspired Algorithms: An Efficient Approach for Frequent Path Mining,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1445-1452, 2019.

MLA Style Citation: S.Kiruthika, A. Malathi "A Survey on Bio Inspired Algorithms: An Efficient Approach for Frequent Path Mining." International Journal of Computer Sciences and Engineering 7.5 (2019): 1445-1452.

APA Style Citation: S.Kiruthika, A. Malathi, (2019). A Survey on Bio Inspired Algorithms: An Efficient Approach for Frequent Path Mining. International Journal of Computer Sciences and Engineering, 7(5), 1445-1452.

BibTex Style Citation:
@article{Malathi_2019,
author = {S.Kiruthika, A. Malathi},
title = {A Survey on Bio Inspired Algorithms: An Efficient Approach for Frequent Path Mining},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1445-1452},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4428},
doi = {https://doi.org/10.26438/ijcse/v7i5.14451452}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.14451452}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4428
TI - A Survey on Bio Inspired Algorithms: An Efficient Approach for Frequent Path Mining
T2 - International Journal of Computer Sciences and Engineering
AU - S.Kiruthika, A. Malathi
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1445-1452
IS - 5
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
364 229 downloads 100 downloads
  
  
           

Abstract

Bio inspired algorithm plays a major role in data mining. The scope of the bio-inspired algorithm is very enormous, it provides major advantages to solve many computational problems. Bio-inspired and frequent path mining is embedded to solve critical problems in data mining. Frequent patterns in a data stream can provide an important basis for decision making and applications. This survey paper represents the applications bio-inspired algorithms, comparative study of Swarm based algorithms, which includes Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search (CS), Artificial Bee Colony (ABC), and Firefly algorithm, which enhance the performance to predict their competent frequent paths.

Key-Words / Index Term

Bioinspired, Swarm algorithms, Evolutionary programming, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search(CS), Artificial Bee Colony (ABC), Firefly algorithm

References

[1] Bellaachia, Abdelghani, and Anasse Bari, "SFLOSCAN: A biologically-inspired data mining framework for community identification in dynamic social networks”,In the preceedings of 2001 IEEE Symposium on Swarm Intelligence, Paris, pp. 1-8, 2011.
[2] Forestiero, Agostino, Clara Pizzuti, and Giandomenico Spezzano,"Flockstream: “A bio-inspired algorithm for clustering evolving data streams”, In the proceedings of 2009 21st IEEE International Conference on Tools with Artificial Intelligence, USA, pp. 1-8. IEEE, 2009.
[3] Lan, Kun, Dan-tong Wang, Simon Fong, Lian-sheng Liu, Kelvin KL Wong, and NilanjanDey, "A survey of data mining and deep learning in bioinformatics”, Journal of medical systems Vol.42, no. 8, 2018.
[4] Cui, Zhihua, Rajan Alex, RajendraAkerkar, and Xin-She Yang, "Recent advances on bioinspired computation”,The Scientific World Journal ,2014.
[5] UpekaPremaratne ,JagathSamarabandu, and Tarlochan Sidhu, “A New Biologically Inspired Optimization Algorithm”,In the proceedings of Fourth International Conference on Industrial and Information Systems, (ICIIS), Sri Lanka, pp.28-31 ,2009.
[6] Alam, Shafiq, Gillian Dobbie, Yun Sing Koh, and Saeed urRehman, Biologically Inspired Techniques for Data Mining: “A Brief Overview of Particle Swarm Optimization for KDD”,In Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, IGI Global,pp. 1-10, 2014.
[7] Chen, Yi-Ting, Jeng-Shyang Pan, Shu-Chuan Chu, and Mong-Fong Horng. "Bio-inspired Evolutionary Computing with Context-Awareness and Collective-Effect",In the proceedings of International Conference on Technologies and Applications of Artificial Intelligence, Springer, pp. 99-113, 2014.
[8] Kar, Arpan Kumar, “Bio-inspired computing:A review of algorithms and scope of applications." Expert Systems with Applications No.59, pp. 20-32,2016.
[9] Binitha, S., and S. Siva Sathya, "A survey of bio-inspired optimization algorithms”, International Journal of Soft Computing and Engineering , no. 2, pp.137-151,2012.
[10] Jafar, OA Mohamed, and R. Sivakumar, "A study of the bio-inspired algorithm to data clustering using different distance measures”, International Journal of Computer Applications Vol.66, no. 12 ,pp. 33-35,2013.
[11] S. Elsayed, R. Sarker, “Differential evolution framework for big data optimization”, MemeticComput,Vol.8 ,No.1,pp. 17–33,2016.
[12] A.Alex Freitas, “A Review of Evolutionary Algorithms for Data Mining”,Soft Computing for Knowledge Discovery and Data Mining,springer, Newyork, pp. 61-93, 2007.
[13] Shah_Hosseini, “Problem solving by intelligent water drops”,In the preceedings of IEEE Congress on Evolutionary Computation,CEC 2007.
[14] https://www.researchgate.net/figure/Pseudo-code-for-the-evolutionary-programming-EP-algorithm.
[15] Tajuddin MFN, Ayob SM, Salam Z, Saad MS, “Evolutionary based maximum power point tracking technique using differential evolution algorithm”,Energy Buildings, Vol.67,pp.245–52,2013.
[16] L.Meghana.,R.Jaya. “Swarm Intelligence Algorithms - A Survey”, IJCSE, Volume.6, Issue.2, pp.184-187,2018.
[17] L. Wang, H. Geng, P. Liu, K. Lu, J. Kolodziej, R. Ranjan, and A.Y. Zomaya, “Particle swarm optimization based dictionary learning for remote sensing big data”, Knowledge.-Based Systems. Vol.79,pp. 43-50,2015.
[18] https://en.wikipedia.org/wiki/Particle_swarm_optimization.
[19] S. D. Shtovba, “Ant Algorithms Theory and Applications”, journal programming and computing software,Vol. 31, Issue. 4, 2005.
[20] Andreas Holzinge, “Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach”, In the preceedings of IFIP International Cross Domain Conference and Workshop,(CD-ARES),Austria,Springer, pp.81-95,2016.
[21] https://www.researchgate.net/figure/Pseudocode-of-the-cuckoo-search-algorithm.
[22] X. S. Yang, S. Deb, “Cuckoo search: Recent advances and applications,” Neural Computing and Applications, vol. 24, no. 1, pp. 169–174, 2014.
[23] https://www.researchgate.net/figure/Pseudo-code-of-Artificial-Bee-Colony-algorithm-for-data-clustering.
[24] DervisKaraboga, BeyzaGorkemli, CelalOzturk ,and NurhanKaraboga, “A comprehensive survey: artificial bee colony (ABC) algorithm and applications”, Journal Artificial Intelligence Review, vol. 42, Issue. 1, pp. 21–57, 2014.
[25] Nidhi Gondalia, Foram Joshi , Nirali Makad “A Novel Approach of Image Ranking Based On Enhanced Artificial Bee Colony Algorithm”, International Journal of Scientific in Recent Sciences (ISROSET), Vol. 1, Issue 1, 2013.
[26] Bahriye Akay, DervisKaraboga,” Artificial bee colony algorithm for large-scale problems and engineering design optimization”, Journal of Intelligent Manufacturing ,Vol.23, Issue.4, pp 1–14,2012.
[27] G.R. Shahmohammadi, Kh..Mohammadi, “Key Management in Hierarchical Sensor Networks Using Improved Evolutionary Algorithm”, IJSRNSC, Vol.4, Issue.2, 2016.
[28] X. S. Yang ,S. Deb, “Two-stage eagle strategy with differential evolution,” International Journal of Bio-Inspired Computation, vol. 4, no. 1, pp. 1–5, 2012.
[29] V.Selvi, R.Umarani, “Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques”, International Journal of Computer Applications , Vol. 5– No.4, 2010.
[30] Ashraf Darwish,” Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications”, Vol.3, Issue.2, pp. 231-246, 2018.