Abstract
Data classification in supervised learning is the process of classifying data for data mining task that helps to analyse data for decision-making. The objective of a classification model is to correctly predict the categorical class labels of known/unknown instances. In machine learning for data mining applications, the classification models are trained based on labelled training datasets. In this paper, we have investigated if we can build a classification model based on the similarities of the instances instead of class labels of instances. Data labelling is always very costly and time-consuming process, and it becomes a very difficult task if the data is big data. The proposed approach clusters the big data and builds the classifier based on the clusters without considering the class labels, which basically improve the performance of the classifier. However, we can relate to the clusters with class labels. We have collected 10 big data from the UC Irvine machine learning repository for experimental analysis and applied three popular decision tree induction algorithms: ID3 (Iterative Dichotomiser 3), C4.5 (extension of ID3 algorithm), and CART (Classification and Regression Tree) for classifier construction.
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
Similar content being viewed by others
References
Aggarwal CC, Reddy CK (eds) (2013) Data clustering: algorithms and applications. Chapman and Hall/CRC data mining and knowledge discovery series. Chapman and Hall/CRC, Boca Raton
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall/CRC, Boca Raton
Chen X, Ye Y, Xu X, Huang JZ (2012) A feature group weighting method for subspace clustering of high-dimensional data. Pattern Recognit 45(1):434–446
Dheeru D, Taniskidou EK (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml
Fan W, Bifet A (2013) Mining big data: current status, and forecast to the future. ACM SIGKDD Explor Newsl 14(2):1–5
Farid DM, Al-Mamun MA, Manderick B, Nowe A (2016) An adaptive rule-based classifier for mining big biological data. Exp Syst Appl 64:305–316
Farid DM, Nowé A, Manderick B (2016) A feature grouping method for ensemble clustering of high-dimensional genomic big data. In: Future technologies conference, San Francisco, United States, pp 260–268
Farid DM, Rahman CM (2013) Assigning weights to training instances increases classification accuracy. Int J Data Min Knowl Manag Process 3(1):129–135
Farid DM, Rahman CM (2013) Mining complex data streams: discretization, attribute selection and classification. J Adv Inf Technol 4(3):129–135
Farid DM, Zhang L, Hossain A, Rahman CM, Strachan R, Sexton G, Dahal K (2013) An adaptive ensemble classifier for mining concept drifting data streams. Exp Syst Appl 40(15):5895–5906
Farid DM, Zhang L, Rahman CM, Hossain M, Strachan R (2014) Hybrid decision tree and naïve bayes classifiers for multi-class classification tasks. Exp Syst Appl 41(4):1937–1946
Han J, Kamber M, Pei J (2011) Data mining concepts and techniques, 3rd edn. Morgan Kaufmann, Waltham
Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognit Lett 31(8):651–666
L’heureux A, Grolinger K, Elyamany HF, Capretz MAM (2017) Machine learning with big data: challenges and approaches. IEEE Access 5:7776–7797
Özköse H, Arı ES, Gencer C (2015) Yesterday, today and tomorrow of big data. Procedia-Soc Behav Sci 195:1042–1050
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M (2011) Édouard Duchesnay: Scikit-learn: Machine learning in python. J Mach Learn Res 12:2825–2830. http://dl.acm.org/citation.cfm?id=1953048.2078195
Quinlan J (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
Quinlan JR (1986) Induction of decision tree. Mach Learn 1(1):81–106
Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Amsterdam
Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khan, S.S., Ahamed, S., Jannat, M., Shatabda, S., Farid, D.M. (2020). Classification by Clustering (CbC): An Approach of Classifying Big Data Based on Similarities. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_50
Download citation
DOI: https://doi.org/10.1007/978-981-13-7564-4_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7563-7
Online ISBN: 978-981-13-7564-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)