Skip to main content

Analysis of k-means clustering approach on the breast cancer Wisconsin dataset



Breast cancer is one of the most common cancers found worldwide and most frequently found in women. An early detection of breast cancer provides the possibility of its cure; therefore, a large number of studies are currently going on to identify methods that can detect breast cancer in its early stages. This study was aimed to find the effects of k-means clustering algorithm with different computation measures like centroid, distance, split method, epoch, attribute, and iteration and to carefully consider and identify the combination of measures that has potential of highly accurate clustering accuracy.


K-means algorithm was used to evaluate the impact of clustering using centroid initialization, distance measures, and split methods. The experiments were performed using breast cancer Wisconsin (BCW) diagnostic dataset. Foggy and random centroids were used for the centroid initialization. In foggy centroid, based on random values, the first centroid was calculated. For random centroid, the initial centroid was considered as (0, 0).


The results were obtained by employing k-means algorithm and are discussed with different cases considering variable parameters. The calculations were based on the centroid (foggy/random), distance (Euclidean/Manhattan/Pearson), split (simple/variance), threshold (constant epoch/same centroid), attribute (2–9), and iteration (4–10). Approximately, 92 % average positive prediction accuracy was obtained with this approach. Better results were found for the same centroid and the highest variance. The results achieved using Euclidean and Manhattan were better than the Pearson correlation.


The findings of this work provided extensive understanding of the computational parameters that can be used with k-means. The results indicated that k-means has a potential to classify BCW dataset.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136(5):E359–E386

    Article  CAS  PubMed  Google Scholar 

  2. Dubey AK, Gupta U, Jain S (2015) Breast cancer statistics and prediction methodology: a systematic review and analysis. Asian Pac J Cancer Prev 16(10):4237–4245

    Article  PubMed  Google Scholar 

  3. Dubey AK, Gupta U, Jain S (2014) A Survey on Breast Cancer Scenario and Prediction Strategy. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA), 2014. Springer International Publishing, pp 367–375

  4. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large data bases, VLDB 1994. vol 1215. pp 487–499

  5. Jain R (2015) Introduction to data mining techniques. Accessed 22 April 2015

  6. Alpaydin E (2014) Introduction to machine learning. MIT press, Cambridge, Massachusetts, United States

  7. Bradley PS, Fayyad UM (1998) Refining initial points for k-means clustering. In: Proceedings of the 15th international conference on machine learning (ICML), Morgan Kaufmann, San Francisco, vol 98. pp 91–99

  8. Mary C, Raja SK (2009) Refinement of clusters from k-means with ant colony optimization. J Theor Appl Inf Technol 6(4):28–32

    Google Scholar 

  9. Wang C, Machiraju R, Huang K (2014) Breast cancer patient stratification using a molecular regularized consensus clustering method. Methods 67(3):304–312

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rahideh A, Shaheed MH (2011) Cancer classification using clustering based gene selection and artificial neural networks. In: IEEE 2nd international conference on control, instrumentation and automation (ICCIA), 2011. pp 1175–1180

  11. Vanisri D, Loganathan C (2010) Fuzzy pattern cluster scheme for breast cancer datasets. In: IEEE international conference on communication and computational intelligence (INCOCCI), 2010. pp 410–414

  12. Festa P (2013) A biased random-key genetic algorithm for data clustering. Math Biosci 245(1):76–85

    Article  CAS  PubMed  Google Scholar 

  13. Chen CH (2014) A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection. Appl Soft Comput 20:4–14

    Article  Google Scholar 

  14. Wei D, Jiang Q, Wei Y, Wang S (2012) A novel hierarchical clustering algorithm for gene sequences. BMC Bioinform 13(1):174

    Article  Google Scholar 

  15. Ahmad FK, Yusoff N (2013) Classifying breast cancer types based on fine needle aspiration biopsy data using random forest classifier. In: IEEE 13th international conference on intelligent systems design and applications (ISDA), 2013. pp 121–125

  16. Bache K, Lichman M (2013) UCI machine learning repository. 1990:92.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ashutosh Kumar Dubey.

Ethics declarations

Conflict of interest

The authors Ashutosh Kumar Dubey, Umesh Gupta, and Sonal Jain declare that they have no conflict of interest. The manuscript does not contain clinical studies or patient data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dubey, A.K., Gupta, U. & Jain, S. Analysis of k-means clustering approach on the breast cancer Wisconsin dataset. Int J CARS 11, 2033–2047 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Breast cancer
  • Breast cancer Wisconsin (BCW) diagnostic dataset
  • K-means
  • Foggy and random centroid