Abstract
Patterns or models in data can be found using data mining algorithms. This is a knowledge discovery process in which data mining is involved. It is a scientific method which is intended to examine massive data, so as to find out the systematic relationships and consistent patterns among variables and further check for the accuracy of the findings. This can be done by taking new subsets of data and applying the detected patterns to them. The core part of the data mining techniques is classification. In classification, in order to develop a model which will categorize the population of records, we make use of a set of pre-classified examples. The techniques of classification use the model which is built on basis of training data and apply it to test data. “Breast cancer Wisconsin data set is used as a training set.” There is an open source data mining tool named WEKA, which consists of implementation of data mining algorithms. By making use of WEKA we have compared the well-known classification algorithms that are decision tree and Bayesian algorithms. It is concluded that decision tree classification algorithm got high accuracy compared to Bayesian classification algorithm.
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
Tomar, D., Agarwal, S.: A survey on data mining approaches for healthcare. Int. J. Bio-Sci. Bio-Technol. 5(5), 241–266 (2013)
Sujatha, G., Usha Rani, K.: A survey on effectiveness of data mining techniques on cancer data sets. Int. J. Eng. Sci. Res. 04(1), 1298–1304 (2013)
Utomo, C.P., Kardiana, A., Yuliwulandari, R.: Breast cancer diagnosis using artificial neural networks with extreme learning techniques. Int. J. Adv. Res. Artif. Intell. 3(7) (2014). http://dx.doi.org/10.14569/IJARAI.2014.030703
Padmapriya, B.: A survey on breast cancer analysis using data mining techniques. IEEE. ISBN No: 978-1-4799-3975-6, 2014
Mennour, R.: Drug discovery for breast cancer based on big data analytics techniques. IEEE (2015)
Arya, C.: Expert system for breast cancer diagnosis: a survey. ICCCI. ISBN No: 978-1-4673-6680-9 (2016)
Xiangchun, K.X.: Analysis of breast cancer using data mining & statistical techniques. IEEE. ISBN No: 0-7695-2294-7 (2005)
Sivagami, P.: Supervised learning approach for breast cancer classification. Int. J. Emerg. Trends Technol. Comput. Sci. 1(4), 115–129 (2012)
Rajesh, K., Anand, S.: Analysis of SEER dataset for breast cancer diagnosis using C4.5 classification algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 1(2), 72–77 (2012)
Babagholami-Mohamadabadi, B., Jourabloo, A., Zarghami, A., Kasaei, S.: A bayesian framework for sparse representation-based 3-d human pose estimation. IEEE Signal Process. Lett. 21(3), 297–300 (2014)
Ramakrishna Murty, M., Murthy, J.V.R., Prasad Reddy, P.V.G.D: Text document classification based on a least square support vector machines with singular value decomposition. Int. J. Comput. Appl. (IJCA) (indexed by DOAJ, Informatics, ProQuest CSA research database) 27(7), 21–26 (2011). ISBN 978-93-80864-56-6, https://doi.org/10.5120/3312-4540
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
Sri, M.N., Priyanka, J.S.V.S.H., Sailaja, D., Ramakrishna Murthy, M. (2019). A Comparative Analysis of Breast Cancer Data Set Using Different Classification Methods. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_17
Download citation
DOI: https://doi.org/10.1007/978-981-13-1921-1_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1920-4
Online ISBN: 978-981-13-1921-1
eBook Packages: EngineeringEngineering (R0)