Abstract
Data analysis has become a critical success factor for companies in all areas. Hence, it is necessary to quickly gain knowledge from available datasets, which is becoming especially challenging in times of big data. Typical data mining tasks like cluster analysis are very time consuming even if they run in highly parallel environments like Spark clusters. To support data scientists in explorative data analysis processes, we need techniques to make data mining tasks even more efficient. To this end, we introduce a novel approach to stop clustering algorithms as early as possible while still achieving an adequate quality of the detected clusters. Our approach exploits the iterative nature of many cluster algorithms and uses a metric to decide after which iteration the mining task should stop. We present experimental results based on a Spark cluster using multiple huge datasets. The experiments unveil that our approach is able to accelerate the clustering up to a factor of more than 800 by obliterating many iterations which provide only little gain in quality. This way, we are able to find a good balance between the time required for data analysis and quality of the analysis results.
Similar content being viewed by others
References
Anand SS, Bell DA, Hughes JG (1995) The role of domain knowledge in data mining. In: Proceedings of the fourth international conference on Information and knowledge management-CIKM ’95, pp 37–43. https://doi.org/10.1145/221270.221321
Arthur D, Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, pp 1027–1025. https://doi.org/10.1145/1283383.1283494
Bahmani B, Moseley B, Vattani A, Kumar R, Vassilvitskii S (2012) Scalable K-means++. Proc VLDB Endow 5(7):622–633. https://doi.org/10.14778/2180912.2180915
Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305. https://doi.org/10.1162/153244303322533223
Brachman RJ, Anand T (1994) The process of knowledge discovery in databases: a first sketch. KDD workshop, pp 1–11
Coggins JM, Jain AK (1985) A spatial filtering approach to texture analysis. Pattern Recogn Lett 3(3):195–203. https://doi.org/10.1016/0167-8655(85)90053-4
Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell PAMI–1(2):224–227. https://doi.org/10.1109/TPAMI.1979.4766909
Dunn JC (1974) Well-separated clusters and optimal fuzzy partitions. J Cybern 4(1):95–104. https://doi.org/10.1080/01969727408546059
Elkan C (2003) Using the triangle inequality to accelerate k-means. In: Proceedings of the twentieth international conference on machine learning (ICML-2003), pp 147–153. https://doi.org/10.1016/0026-2714(92)90278-S
Hochbaum DS, Shmoys DB (1985) A best possible heuristic for the k-center problem. Math Oper Res 10(2):180–184. https://doi.org/10.1287/moor.10.2.180
Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River. https://doi.org/10.2307/1268876
Kanungo T, Mount D, Netanyahu N, Piatko C, Silverman R, Wu A (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892. https://doi.org/10.1109/TPAMI.2002.1017616
Kopanas I, Avouris NM, Daskalaki S (2002) The role of domain knowledge in a large scale data mining project. Methods Appl Artif Intell 2308(June 2002):288–299. https://doi.org/10.1007/3-540-46014-4_26
Kotthoff L, Thornton C, Hoos HH, Hutter F, Leyton-Brown K (2016) Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. J Mach Learn Res 17:1–5
Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137. https://doi.org/10.1109/TIT.1982.1056489
Macqueen JB (1967) Some methods for classification and analysis of multivariate observations. Proc Fifth Berkeley Symp Math Stat Probab 1:281–297
Malkomes G, Schaff C, Garnett R (2016) Bayesian optimization for automated model selection. In: International conference on machine learning 2016, AutoML workshop, vol 1, No. Nips, pp 1–7
Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S, Xin D, Xin R, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A (2016) MLlib: machine learning in Apache spark. J Mach Learn Res 17:1–7
Mexicano A, Rodríguez R, Cervantes S, Montes P, Jiménez M, Almanza N, Abrego A (2016) The early stop heuristic: a new convergence criterion for K-means. In: AIP conference proceedings, vol 1738. https://doi.org/10.1063/1.4952103
Pérez J, Mexicano A, Pazos R, Santaolaya R, Hidalgo M, Moreno A, Almanza N (2013) Improvement to the K-means algorithm through a heuristics based on a Bee Honeycomb structure. J Netw Innov Comput ISSN 1:2160–2174
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(C):53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Sculley D (2010) Web-scale K-means clustering. In: Proceedings of the 19th international conference on world wide web WWW 10, p 1177. https://doi.org/10.1145/1772690.1772862
Selim SZ, Ismail MA (1984) K-means type algorithms: a generalized concergence theorem and characterization of local optimality. IEEE Tran Pattern Anal Mach Intell PAMI 6(1):81–87
Sparks ER, Talwalkar A, Haas D, Franklin MJ, Jordan MI, Kraska T (2015) Automating model search for large scale machine learning. In: Proceedings of the sixth ACM symposium on cloud computing-SoCC ’15, pp 368–380. https://doi.org/10.1145/2806777.2806945
Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining-KDD ’13, p 847. https://doi.org/10.1145/2487575.2487629
Vendramin L, Campello RJ, Hruschka ER (2010) Relative clustering validity criteria: a comparative overview. Stat Anal Data Min 3(4):209–235. https://doi.org/10.1002/sam.10080
Acknowledgements
This research was partially funded by the Ministry of Science of Baden-Württemberg, Germany, for the Doctoral Program ‘Services Computing’. Some work presented in this paper was performed within the project ‘INTERACT’ as part of the Software Campus program. This project is funded by the German Federal Ministry of Education and Research (BMBF), Grant No. 01IS17051. Finally, we thank Dennis Tschechlov for his implementation work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fritz, M., Behringer, M. & Schwarz, H. Quality-driven early stopping for explorative cluster analysis for big data. SICS Softw.-Inensiv. Cyber-Phys. Syst. 34, 129–140 (2019). https://doi.org/10.1007/s00450-019-00401-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00450-019-00401-0