Abstract
Particle swarm optimization algorithm is known as a population-based algorithm which actually maintains a population of particles. The particle plays a significant role which represents an effective solution to an optimization problem. The study proposed in the paper intends to integrate PSO with the artificial bee colony (ABC) algorithm. The research inspired from the intelligent biological behavior of swarms where it involves the merits of both the algorithm to perform experimental analysis on the social media data. The hybrid bio-inspired clustering approach is being proposed to apply on social media data which is known to be highly categorical in nature. The result shows that clustering analysis is helpful to classify high dimensional categorical data. Social media analysis effectively can be achieved through clustering which is being demonstrated in the proposed hybrid approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tan PN, Steinbach M, Kumar V (2005) Introduction to data mining. Addison-Wesley, Boston
Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge
Webb A (2002) Statistical pattern recognition. Wiley, New Jersey
Han J (2006) Kamber M data mining: concepts and techniques, 2nd edn. Morgan Kaufmann, Menlo Park
Hathway RJ, Bezdek J (1995) Optimization of clustering criteria by reformulation. IEEE Trans Fuzzy Syst 241–245
Kennedy J, Eberhart R (2001) Swarm intelligence. Morgan Kaufmann
Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: The 2003 congress on evolutionary computation, pp 215–220
Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE swarm intelligence symposium 2006, 12–14 May 2006, Indianapolis, Indiana, USA
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optimiz 39(3):459–471
Xu Chunfan, Duan Haibin (2010) Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft. Pattern Recogn Lett 31(13):1759–1772
Runkler TA, Katz C (2006) Fuzzy clustering by particle swarm optimization. In: 2006 IEEE international conference on fuzzy systems, Canada, pp 601–608
Zhao B (2007) An ant colony clustering algorithm. In: Proceedings of the sixth international conference on machine learning and cybernetics, Hong Kong, pp 3933–3938
Li L, Liu X, Xu M (2007) A novel fuzzy clustering based on particle swarm optimization. In: First IEEE international symposium on information technologies and applications in education, pp 88–90
Gan G, Wu J, Yang Z (2009) A genetic fuzzy k-modes algorithm for clustering categorical data. Expert Syst Appl 36:1615–1620
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948
Pang W, Wang K, Zhou C, Dong L (2004) Fuzzy discrete particle swarm optimization for solving traveling salesman problem. In: Proceedings of the fourth international conference on computer and information technology, IEEE CS Press, pp 796–800
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
Yang Y (1999) An evaluation of statistical approaches to text categorization. J Inform Retr 1:67–88
Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Disc 2:283–304
Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66:846–850
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shrivastava, A., Garg, M.L. (2019). A Hybrid Bio-inspired Clustering Algorithm to Social Media Analysis. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-13-7082-3_10
Download citation
DOI: https://doi.org/10.1007/978-981-13-7082-3_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7081-6
Online ISBN: 978-981-13-7082-3
eBook Packages: EngineeringEngineering (R0)