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Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads

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  • Mark Chiang
  • Boris Mirkin

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  • Mark Chiang & Boris Mirkin, 2010. "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 3-40, March.
    • Handle: RePEc:spr:jclass:v:27:y:2010:i:1:p:3-40
    DOI: 10.1007/s00357-010-9049-5
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    2. Wasito, Ito & Mirkin, Boris, 2006. "Nearest neighbours in least-squares data imputation algorithms with different missing patterns," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 926-949, February.
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    5. Douglas Steinley & Robert Henson, 2005. "OCLUS: An Analytic Method for Generating Clusters with Known Overlap," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 221-250, September.
    6. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    7. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    8. Hand, David J. & Krzanowski, Wojtek J., 2005. "Optimising k-means clustering results with standard software packages," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 969-973, June.
    9. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
    10. McLachlan, G. J. & Khan, N., 2004. "On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 90-105, July.
    11. Evgenia Dimitriadou & Sara Dolničar & Andreas Weingessel, 2002. "An examination of indexes for determining the number of clusters in binary data sets," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 137-159, March.
    12. Glenn Milligan, 1981. "A monte carlo study of thirty internal criterion measures for cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 187-199, June.
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    3. Kaczynska, S. & Marion, R. & Von Sachs, R., 2020. "Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation," LIDAM Discussion Papers ISBA 2020009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Aslani, Mehrdad & Faraji, Jamal & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "A novel clustering-based method for reliability assessment of cyber-physical microgrids considering cyber interdependencies and information transmission errors," Applied Energy, Elsevier, vol. 315(C).
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    6. Dogan Gursoy & Anna Maria Parroco & Raffaele Scuderi, 2013. "An Examination of Tourist Arrivals Dynamics Using Short-Term Time Series Data: A Space—Time Cluster Approach," Tourism Economics, , vol. 19(4), pages 761-777, August.
    7. Al-Augby Salam & Majewski Sebastian & Majewska Agnieszka & Nermend Kesra, 2014. "A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 19-36, December.
    8. Cristina Tortora & Mireille Gettler Summa & Marina Marino & Francesco Palumbo, 2016. "Factor probabilistic distance clustering (FPDC): a new clustering method," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 441-464, December.
    9. J. Fernando Vera & Rodrigo Macías, 2017. "Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 275-294, June.
    10. Muhamad Rizki & Muhammad Zudhy Irawan & Puspita Dirgahayani & Prawira Fajarindra Belgiawan & Retno Wihanesta, 2022. "Low Emission Zone (LEZ) Expansion in Jakarta: Acceptability and Restriction Preference," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
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    12. Zina Taran & Boris Mirkin, 2020. "Exploring patterns of corporate social responsibility using a complementary K-means clustering criterion," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 513-540, July.
    13. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    14. Haoyang Ping & Zhuocheng Li & Xizhu Shen & Haizhen Sun, 2024. "Optimization of Vegetable Restocking and Pricing Strategies for Innovating Supermarket Operations Utilizing a Combination of ARIMA, LSTM, and FP-Growth Algorithms," Mathematics, MDPI, vol. 12(7), pages 1-17, March.
    15. Dogan Gursoy & Anna Maria Parroco & Raffaele Scuderi, 2013. "An examination of tourist arrivals dynamics using short-term time series data: a space-time cluster approach," BEMPS - Bozen Economics & Management Paper Series BEMPS06, Faculty of Economics and Management at the Free University of Bozen.
    16. Meng Li & Jiqiang Liu & Yeping Yang, 2024. "Automated Identification of Sensitive Financial Data Based on the Topic Analysis," Future Internet, MDPI, vol. 16(2), pages 1-17, February.
    17. Sara Dolnicar & Friedrich Leisch, 2017. "Using segment level stability to select target segments in data-driven market segmentation studies," Marketing Letters, Springer, vol. 28(3), pages 423-436, September.
    18. Shouxiang Wang & Pengfei Dong & Yingjie Tian, 2017. "A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost," Energies, MDPI, vol. 10(12), pages 1-17, December.
    19. Boris Mirkin & Soroosh Shalileh, 2022. "Community Detection in Feature-Rich Networks Using Data Recovery Approach," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 432-462, November.
    20. Antonella Pireddu & Angelico Bedini & Mara Lombardi & Angelo L. C. Ciribini & Davide Berardi, 2024. "A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management," IJERPH, MDPI, vol. 21(7), pages 1-26, June.
    21. Matteo Farnè & Angelos T. Vouldis, 2021. "Banks’ business models in the euro area: a cluster analysis in high dimensions," Annals of Operations Research, Springer, vol. 305(1), pages 23-57, October.
    22. Jaehong Yu & Hua Zhong & Seoung Bum Kim, 2020. "An Ensemble Feature Ranking Algorithm for Clustering Analysis," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 462-489, July.

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