Abstract
Conventional stream mining algorithms focus on stand-alone mining tasks. Given the single-pass nature of data streams, it makes sense to maximize throughput by performing multiple complementary mining tasks concurrently. We investigate the potential of concurrent semi-supervised learning on data streams and propose an incremental algorithm called CSL-Stream (Concurrent Semi–supervised Learning of Data Streams) that performs clustering and classification at the same time. Experiments using common synthetic and real datasets show that CSL-Stream outperforms prominent clustering and classification algorithms (D-Stream and SmSCluster) in terms of accuracy, speed and scalability. Moreover, enhanced with a novel active learning technique, CSL-Stream only requires a small number of queries to work well with very sparsely labeled datasets. The success of CSL-Stream paves the way for a new research direction in understanding latent commonalities among various data mining tasks in order to exploit the power of concurrent stream mining.
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
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, C., Han, J., Wang, J., Yu, P.: On Clustering Massive Data Streams: A Summarization Paradigm. Advances in Database Systems, vol. 31, pp. 9–38. Springer US (2007)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 81–92. VLDB Endowment (2003)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for projected clustering of high dimensional data streams. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 852–863. VLDB Endowment (2004)
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for on-demand classification of evolving data streams. IEEE Transactions on Knowledge and Data Engineering 18(5), 577–589 (2006)
Amigo, E., Gonzalo, J., Artiles, J.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval 12(4), 461–486 (2009)
Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 59–68. ACM (2004)
Baum, E., Lang, K.: Query learning can work poorly when a human oracle is used. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 335–340. IEEE Press (1992)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. The Journal of Machine Learning Research 7, 2399–2434 (2006)
Bifet, A., Holmes, G., Kirkby, R.: Moa: Massive online analysis. The Journal of Machine Learning Research 11, 1601–1604 (2010)
Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 19–26. Morgan Kaufmann Publishers Inc. (2001)
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 328–339 (2006)
Chapelle, O., Sindhwani, V., Keerthi, S.S.: Optimization techniques for semi-supervised support vector machines. The Journal of Machine Learning Research 9, 203–233 (2008)
Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2007)
Cuzzocrea, A.: CAMS: OLAPing Multidimensional Data Streams Efficiently. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 48–62. Springer, Heidelberg (2009)
Cuzzocrea, A.: Retrieving Accurate Estimates to OLAP Queries over Uncertain and Imprecise Multidimensional Data Streams. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 575–576. Springer, Heidelberg (2011)
Cuzzocrea, A., Chakravarthy, S.: Event-based lossy compression for effective and efficient olap over data streams. Data & Knowledge Engineering 69(7), 678–708 (2010)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Han, J., Chen, Y., Dong, G., Pei, J., Wah, B., Wang, J., Cai, Y.: Stream cube: An architecture for multi-dimensional analysis of data streams. Distributed and Parallel Databases 18(2), 173–197 (2005)
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106. ACM (2001)
Joachims, T.: Making large-scale support vector machine learning practical, pp. 169–184. MIT Press (1999)
Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis 8(3), 281–300 (2004)
Kriegel, H.-P., Kröger, P., Ntoutsi, I., Zimek, A.: Density Based Subspace Clustering over Dynamic Data. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 387–404. Springer, Heidelberg (2011)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: The ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–12. ACM/Springer (1994)
Martin, E., Hans-Peter, K., Jörg, S., Xiaowei, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)
Masud, M.M., Jing, G., Khan, L., Jiawei, H., Thuraisingham, B.: A practical approach to classify evolving data streams: Training with limited amount of labeled data. In: ICDM, pp. 929–934 (2008)
Melville, P., Mooney, R.J.: Diverse ensembles for active learning (2004)
Park, N.H., Lee, W.S.: Grid-based subspace clustering over data streams. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 801–810. ACM (2007)
Sattar, H., Ying, Y., Zahra, M., Mohammadreza, K.: Adapted one-vs-all decision trees for data stream classification. IEEE Transactions on Knowledge and Data Engineering 21, 624–637 (2009)
Sindhwani, V., Keerthi, S.S.: Large scale semi-supervised linear svms. In: ACM SIGIR, pp. 477–484. ACM (2006)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research 2, 45–66 (2002)
Vilalta, R., Rish, I.: A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 444–455. Springer, Heidelberg (2003)
Wagstaff, K., Cardie, C., Rogers, S.: Constrained k-means clustering with background knowledge. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 577–584. Morgan Kaufmann Inc. (2001)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM (2003)
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 103–114. ACM (1996)
Zhou, A., Cao, F., Qian, W., Jin, C.: Tracking clusters in evolving data streams over sliding windows. Knowledge and Information Systems 15(2), 181–214 (2008)
Zhu, X., Goldberg, A.B., Brachman, R., Dietterich, T.: Introduction to Semi-Supervised Learning. Morgan and Claypool Publishers (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nguyen, HL., Ng, WK., Woon, YK. (2013). Concurrent Semi-supervised Learning with Active Learning of Data Streams. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII. Lecture Notes in Computer Science, vol 7790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37574-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-37574-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37573-6
Online ISBN: 978-3-642-37574-3
eBook Packages: Computer ScienceComputer Science (R0)