Dynamic clustering with binary social spider algorithm for streaming dataset
- 53 Downloads
Technical advancement in various fields like social network, health instruments and astronomical devices poses massive capturing and sensing capacity that enables huge data generations. This demands substantial storage space and voluminous data processing capacity. Streaming data clustering imparts an efficient method for handling this dataset by extracting significant information. In this article, dynamic estimation of clusters in evolving data stream is designed by incorporating swarm optimization technique. One of the recently reported algorithms inspired from the social behavior of spiders residing in huge colonies is reformulated in binary domain. The main contribution is to use the binary social spider optimization (BSSO) for dynamic data clustering of evolving dataset (DSC-BSSO). The proposed work is able to prove efficiency and efficacy as compared to the other recent existing algorithms. BSSO is well tested on various benchmark unimodal, multimodal and binary optimization functions. Results are reported in terms of parametric and nonparametric. The testing of DSC-BSSO is also done on various streaming datasets in terms of time and memory complexity. The proposed work is able to obtain compact and well-separated clusters in less than one-fourth of a minute for about 10,000 samples.
KeywordsDynamic clustering Page-Hinkley statistical test Social spider optimization Wilcoxon’s pair test
The research work is funded by institute fellowship from Ministry of HRD, Govt. of India, to Urvashi P. Shukla to pursue her PhD work at MNIT Jaipur.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with animals or humans performed by any of the authors.
- Abualigah LM, Khader AT, Al-Betar MA, Awadallah MA (2016) A krill herd algorithm for efficient text documents clustering. In: Computer applications and industrial electronics (ISCAIE), 2016 IEEE symposium. IEEE, pp 67–72Google Scholar
- Abualigah LM, Khader AT, Hanandeh ES (2018a) A hybrid strategy for krill herd algorithm with harmony search algorithm to improve the data clustering. Intelli Decis Technol Prepr, 12:1–12Google Scholar
- Falcon R, Almeida M, Nayak A (2011) Fault identification with binary adaptive fireflies in parallel and distributed systems. In: Evolutionary computation (CEC). IEEE Congress, pp 1359–1366Google Scholar
- Firpi HA, Goodman E (2004) Swarmed feature selection. In: In Information Theory, ISIT Proceedings. International Symposium on. IEEE, pp 112–118Google Scholar
- Ghaemi A, Rashedi E, Pourrahimi AM, Kamandar M, Rahdari F (2017) Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm. Biomed Signal Process Control 33:109–118CrossRefGoogle Scholar
- Houck CR, Joines J, Kay MG (1995) A genetic algorithm for function optimization: a Matlab implementation. Ncsu-ie tr 95(09):1–10Google Scholar
- Kanan HR, Faez K, Taheri SM (2007) Feature selection using ant colony optimization (ACO): a new method and comparative study in the application of face recognition system. In: Industrial conference on data mining. Springer, Berlin, pp 63–76Google Scholar
- Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. Computational cybernetics and simulation, IEEE International Conference on, vol 5, pp 4104–4108Google Scholar
- Mouss H, Mouss D, Mouss N, Sefouhi L (2004) Test of Page-Hinkley, an approach for fault detection in an agro-alimentary production system. In: Proceedings of the Asian control conference, vol 2. IEEE, pp 815–818Google Scholar
- Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang X-S (2012) BBA: a binary bat algorithm for feature selection. In: 2012, IEEE 25th SIBGRAPI conference on graphics, Patterns and Images, pp 291–297Google Scholar
- Nozarian S, Soltanpoor H, VafaeiJahan M (2011) A binary model on the basis of cuckoo search algorithm in order to solve the problem of knapsack 1–0. In: International conference of sysem engineering and modeling (ICSEM), pp 67–71Google Scholar
- Rodrigues D, Pereira LAM, Almeida TNS, Papa JP, Souza AN, Ramos CC, Yang X-S (2013) BCS: a binary cuckoo search algorithm for feature selection. In: Circuits and systems (ISCAS), IEEE international symposium, pp 465–468Google Scholar
- Rodrigues D, Yang X-S, De Souza AN, Papa JP (2015) Binary flower pollination algorithm and its application to feature selection. In: Recent advances in swarm intelligence and evolutionary computation. Springer, pp 85–100Google Scholar
- Shehab M, Khader AT, Al-Betar MA, Abualigah LM (2017) Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In: Information technology (ICIT), 2017 8th international conference. IEEE, pp 36–43Google Scholar
- Shukla UP, Nanda SJ (2016) Cluster analysis of evolving data streams using centroid initialization methods. In: Electrical, computer and electronics engineering (UPCON), 2016 IEEE Uttar Pradesh section international conference, pp 624–629Google Scholar
- Wang L, Xu Y, Mao Y, Fei M (2010) A discrete harmony search algorithm. In: Life system modeling and intelligent computing. Springer, Berlin, pp 37–43Google Scholar