{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:08:32Z","timestamp":1764688112944,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819749843"},{"type":"electronic","value":"9789819749850"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":197,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper introduces a novel K-means clustering algorithm, an advancement on the conventional Big-means methodology. The proposed method efficiently integrates parallel processing, stochastic sampling, and competitive optimization to create a scalable variant designed for big data applications. It addresses scalability and computation time challenges typically faced with traditional techniques. The algorithm adjusts sample sizes dynamically for each worker during execution, optimizing performance. Data from these sample sizes are continually analyzed, facilitating the identification of the most efficient configuration. By incorporating a competitive element among workers using different sample sizes, efficiency within the Big-means algorithm is further stimulated. In essence, the algorithm balances computational time and clustering quality by employing a stochastic, competitive sampling strategy in a parallel computing setting.<\/jats:p>","DOI":"10.1007\/978-981-97-4985-0_18","type":"book-chapter","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:07:08Z","timestamp":1721041628000},"page":"224-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Superior Parallel Big Data Clustering Through Competitive Stochastic Sample Size Optimization in\u00a0Big-Means"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7283-5144","authenticated-orcid":false,"given":"Rustam","family":"Mussabayev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1105-5990","authenticated-orcid":false,"given":"Ravil","family":"Mussabayev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"18_CR1","unstructured":"Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SODA \u201907, pp. 1027\u20131035. Society for Industrial and Applied Mathematics, USA (2007)"},{"issue":"7","key":"18_CR2","doi-asserted-by":"publisher","first-page":"622","DOI":"10.14778\/2180912.2180915","volume":"5","author":"B Bahmani","year":"2012","unstructured":"Bahmani, B., Moseley, B., Vattani, A., Kumar, R., Vassilvitskii, S.: Scalable k-means++. Proc. VLDB Endow. 5(7), 622\u2013633 (2012)","journal-title":"Proc. VLDB Endow."},{"key":"18_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1","volume-title":"Pattern Recognition with Fuzzy Objective Function Algorithms","author":"JC Bezdek","year":"1981","unstructured":"Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)"},{"key":"18_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIT48885.2019.9038535","volume-title":"Comparative Performance of Seeding Methods for k-Means Algorithm","author":"ME Celebi","year":"2013","unstructured":"Celebi, M.E.: Comparative Performance of Seeding Methods for k-Means Algorithm. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1109\/ICCIT48885.2019.9038535"},{"issue":"5","key":"18_CR5","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/34.1000236","volume":"24","author":"D Comaniciu","year":"2002","unstructured":"Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603\u2013619 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6) (1990)","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Ding, C., He, X.: K-means clustering via principal component analysis. In: Proceedings of the Twenty-First International Conference on Machine Learning (2004)","DOI":"10.1145\/1015330.1015408"},{"issue":"25","key":"18_CR8","doi-asserted-by":"publisher","first-page":"14863","DOI":"10.1073\/pnas.95.25.14863","volume":"95","author":"MB Eisen","year":"1998","unstructured":"Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95(25), 14863\u201314868 (1998)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"18_CR9","unstructured":"Forgy, E.W.: Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Technical report RM-5437-PR, RAND Corporation (1965)"},{"issue":"3\u20135","key":"18_CR10","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","volume":"486","author":"S Fortunato","year":"2010","unstructured":"Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3\u20135), 75\u2013174 (2010)","journal-title":"Phys. Rep."},{"key":"18_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7"},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31, 651\u2013666 (2010)","journal-title":"Pattern Recogn. Lett."},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Karmitsa, N., Bagirov, A.M., Taheri, S.: Clustering in large data sets with the limited memory bundle method. Pattern Recogn. (2018)","DOI":"10.1016\/j.patcog.2018.05.028"},{"key":"18_CR14","unstructured":"MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.\u00a01, pp. 281\u2013297 (1967)"},{"key":"18_CR15","unstructured":"MacQueen, J.B.: Some methods for classification and analysis of multivariate observations 1(14) (1967)"},{"issue":"4","key":"18_CR16","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1007\/s11227-017-2213-5","volume":"74","author":"A Marowka","year":"2018","unstructured":"Marowka, A.: Python accelerators for high-performance computing. J. Supercomput. 74(4), 1449\u20131460 (2018)","journal-title":"J. Supercomput."},{"key":"18_CR17","unstructured":"Mussabayev, R., Mussabayev, R.: Strategies for parallelizing the big-means algorithm: a comprehensive tutorial for effective big data clustering (2023)"},{"key":"18_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109269","volume":"137","author":"R Mussabayev","year":"2023","unstructured":"Mussabayev, R., Mladenovic, N., Jarboui, B., Mussabayev, R.: How to use k-means for big data clustering? Pattern Recogn. 137, 109269 (2023). https:\/\/doi.org\/10.1016\/j.patcog.2022.109269","journal-title":"Pattern Recogn."},{"key":"18_CR19","unstructured":"Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849\u2013856 (2002)"},{"key":"18_CR20","unstructured":"Pelleg, D., Moore, A.: X-means: extending k-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference on Machine Learning, pp. 727\u2013734 (2000)"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1177\u20131178 (2010)","DOI":"10.1145\/1772690.1772862"},{"key":"18_CR22","unstructured":"Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining (2000)"},{"key":"18_CR23","unstructured":"Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation. USENIX Association (2012)"},{"issue":"2","key":"18_CR24","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/235968.233324","volume":"25","author":"T Zhang","year":"1996","unstructured":"Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for large databases. ACM SIGMOD Rec. 25(2), 103\u2013114 (1996)","journal-title":"ACM SIGMOD Rec."}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-4985-0_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:16:15Z","timestamp":1721042175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-4985-0_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819749843","9789819749850"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-4985-0_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"16 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ras Al Khaimah","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Arab Emirates","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2024\/index.php#about","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}