| [1] Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques (3rd edition). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2011.[2] Gaber M, Zaslavsky A, Krishnaswamy S. Mining data streams: A review. ACM SIGMOD Record, 2005, 34(2): 1826.[3] Cao F, Ester M, Qian W, Zhou A. Density-based clustering over an evolving data stream with noise. In Proc. the 2006 SIAM Conference on Data Mining, April 2006, pp.328-339.[4] Chen Y, Tu L. Density-based clustering for real-time stream data. In Proc. the 13th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Aug. 2007, pp.133-142.[5] Aggarwal C C (ed.). Data Streams: Models and Algorithms. New York, USA: Springer, 2007.[6] Hahsler M, Dunham M H. Temporal structure learning for clustering massive data streams in real-time. In Proc. the 11th SIAM Conference on Data Mining, April 2011, pp.664675.[7] O?allaghan L, Mishra N, Meyerson A et al. Streaming-data algorithms for high-quality clustering. In Proc. the 18th Int. Conf. Data Engineering, Feb. 26-Mar. 1, 2002, pp.685-694.[8] Barbará D. Requirements for clustering data streams. SIGKDD Explorations Newsletter, 2002, 3(2): 23-27.[9] Guha S, Meyerson A, Mishra N et al. Clustering data streams: Theory and practice. IEEE Trans. Knowledge and Data Engineering, 2003, 15(3): 515-528.[10] Aggarwal C C, Han J, Wang J, Yu P S. A framework for clustering evolving data streams. In Proc. the 29th International Conference on Very Large Data Bases, Sept. 2003, pp.81-92.[11] Ackermann M R, Lammersen C, Märtens M, Raupach C, Sohler C, Swierkot K. StreamKM++: A clustering algorithm for data streams. In Proc. the 12th Workshop on Algorithm Engineering and Experiments, Jan. 2010, pp.173-187.[12] Ikonomovska E, Loskovska S, Gjorgjevik D. A survey of stream data mining. In Proc. the 8th National Conference with International Participation, Sept. 2007, pp.19-25.[13] Gaber M, Zaslavsky A, Krishnaswamy S. Data stream mining. Data Mining and Knowledge Discovery Handbook, 2010, pp.759-787.[14] Babcock B, Babu S, Datar M, Motwani R, Widom J. Models and issues in data stream systems. In Proc. the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, June 2002, pp.1-16.[15] Jain A K, Dubes R C. Algorithms for Clustering Data. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1988.[16] Jain A K. Data clustering: 50 years beyond K-means. Pattern Recognition Letter, 2010, 31(8): 651-666.[17] Mahdiraji A. Clustering data stream: A survey of algorithms. Int. J. Knowledge-Based and Intelligent Engineering Systems, 2009, 13(2): 39-44.[18] Amini A, Wah T, Saybani M et al. A study of density-grid based clustering algorithms on data streams. In Proc. the 8th Int. Conf. Fuzzy Systems and Knowledge Discovery, July 2011, pp.1652-1656.[19] Amini A, Wah T Y. Density micro-clustering algorithms on data streams: A review. In Proc. Int. Multiconf. Data Mining and Applications, March 2011, pp.410-414.[20] Amini A, Wah T Y. A comparative study of density-based clustering algorithms on data streams: Micro-clustering approaches. In Lecture Notes in Electrical Engineering 110, Ao S, Castillo O, Huang X (eds.), Springer, 2012, pp.275-287.[21] Aggarwal C C. A survey of stream clustering algorithms. In Data Clustering: Algorithms and Applications, Aggarwal C C, Reddy C (eds.), CRC Press, 2013, pp.457-482.[22] Han J, Kamber M. Data Mining: Concepts and Techniques (2nd edition). Morgan Kaufmann, 2006.[23] MacQueen J. Some methods for classification and analysis of multivariate observations. In Proc. the 5th Berkeley Symposium on Mathematical Statistics and Probability, June 21July 18, 1967, pp.281-297.[24] Lloyd S P. Least squares quantization in PCM. IEEE Transactions on Information Theory, 1982, 28(2): 129-137.[25] Guha S, Mishra N, Motwani R, O'Callaghan L. Clustering data streams. In Proc. the 41st Annual Symposium on Foundations of Computer Science, Nov. 2000, pp.359-366.[26] Zhang T, Ramakrishnan R, Livny M. BIRCH: An efficient data clustering method for very large databases. In Proc. the 1996 ACM SIGMOD International Conference on Management of Data, June 1996, pp.103-114.[27] Karypis G, Han E, Kumar V. Chameleon: Hierarchical clustering using dynamic modeling. Computer, 1999, 32(8): 6875.[28] Kranen P, Assent I, Baldauf C, Seidl T. The clustree: Indexing micro-clusters for anytime stream mining. Knowl. Inf. Syst., 2011, 29(2): 249-272.[29] Wang W, Yang J, Muntz R R. STING: A statistical information grid approach to spatial data mining. In Proc. the 23rd Int. Conf. Very Large Data Bases, Aug. 1997, pp.186-195.[30] Sheikholeslami G, Chatterjee S, Zhang A. Wavecluster: A wavelet-based clustering approach for spatial data in very large databases. The VLDB Journal, 2000, 8(3/4): 289-304.[31] Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. ACM SIGMOD Record, 1998, 27(2): 94-105.[32] Tu L, Chen Y. Stream data clustering based on grid density and attraction. ACM Transactions on Knowledge Discovery Data, 2009, 3(3): Article No. 12.[33] Wan L, Ng W K, Dang X H et al. Density-based clustering of data streams at multiple resolutions. ACM Trans. Knowledge Discovery from Data, 2009, 3(3): Article No. 14.[34] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 1977, 39(1): 1-38.[35] Dang X, Lee V, Ng W K et al. An EM-based algorithm for clustering data streams in sliding windows. In Proc. the Int. Conf. Database Systems for Advanced Applications, Apr. 2009, pp.230-235.[36] Ester M, Kriegel H, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. the 2nd International Conference on Knowledge Discovery and Data Mining, Aug. 1996, pp.226-231.[37] Ankerst M, Breunig M M, Kriegel H P, Sander J. Optics: Ordering points to identify the clustering structure. ACM SIGMOD Record, 1999, 28(2): 49-60.[38] Hinneburg A, Keim D A. An efficient approach to clustering in large multimedia databases with noise. In Proc. the 4th KDD, Sept. 1998, pp.58-65.[39] Matysiak M. Data stream mining: Basic methods and techniques. Technical Report, RWTH Aachen University, 2012.[40] Zhou A, Cao F, Qian W, Jin C. Tracking clusters in evolving data streams over sliding windows. Knowledge and Information Systems, 2008, 15(2): 181-214.[41] Ren J, Ma R. Density-based data streams clustering over sliding windows. In Proc. the 6th Int. Conf. Fuzzy systems and Knowledge Discovery, Aug. 2009, pp.248-252.[42] Charikar M, O'Callaghan L, Panigrahy R. Better streaming algorithms for clustering problems. In Proc. the 35th Annual ACM Symp. Theory of Computing, June 2003, pp.30-39.[43] Gao J, Li J, Zhang Z, Tan P N. An incremental data stream clustering algorithm based on dense units detection. In Proc. the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, May 2005, pp.420-425.[44] Aggarwal C C, Han J, Wang J, Yu P S. A framework for projected clustering of high dimensional data streams. In Proc. the 30th International Conference on Very Large Data Bases, Volume 30, Aug. 29-Sept. 3, 2004, pp.852-863.[45] Aggarwal C C, Han J, Wang J, Yu P S. On high dimensional projected clustering of data streams. Data Mining and Knowledge Discovery, 2005, 10(3): 251-273.[46] Babcock B, Datar M, Motwani R, O'Callaghan L. Maintaining variance and k-medians over data stream windows. In Proc. the 22nd ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems, June 2003, pp.234-243.[47] Ng W, Dash M. Discovery of frequent patterns in transactional data streams. In Lecture Notes in Computer Science 6380, Hameurlain A, Küng J,Wagner R et al. (eds.), Springer Berlin/Heidelberg, 2010, pp.1-30.[48] Vitter J S. Random sampling with a reservoir. ACM Trans. Math. Softw., 1985, 11(1): 37-57.[49] Garofalakis M, Gehrke J, Rastogi R. Querying and mining data streams: You only get one look: A tutorial. In Proc. the 2002 ACM SIGMOD Int. Conf. Management of Data, June 2002, pp.635-635.[50] Aggarwal C C, Yu P S. A survey of synopsis construction in data streams. In Advances in Database Systems 31, Aggarwal C C (ed.), Springer, 2007, pp.169-207.[51] Garofalakis M N. Wavelets on streams. In Encyclopedia of Database Systems, Springer US, 2009, pp.3446-3451.[52] Gilbert A C, Kotidis Y, Muthukrishnan S, Strauss M J. Onepass wavelet decompositions of data streams. IEEE Trans. Knowl. and Data Eng., 2003, 15(3): 541-554.[53] Gama J, Gaber M M (eds.). Learning from Data Streams { Processing Techniques in Sensor Networks. Springer, 2007.[54] Rosset S, Inger A. KDD-cup 99: Knowledge discovery in a charitable organization's donor database. SIGKDD Explorations Newsletter, 2000, 1(2): 85-90.[55] Hubert L J, Levin J R. A general statistical framework for assessing categorical clustering in free recall. Psychological Bulletin, 1976, 83(6): 1072-1080.[56] Kaufman L, Rousseeuw P J. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience, 2005.[57] Wu J, Xiong H, Chen J. Adapting the right measures for K-means clustering. In Proc. the 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, June 2009, pp.877-886.[58] Rand W M. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 1971, 66(336): 846-850.[59] Zhao Y, Karypis G. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning, 2004, 55(3): 311-331.[60] Dongen S. Performance criteria for graph clustering and Markov cluster experiments. Technical Report, National Research Institute for Mathematics and Computer Science, Stichting Mathematisch Centrum, Netherlands, 2000.[61] Rosenberg A, Hirschberg J. V-measure: A conditional entropy-based external cluster evaluation measure. In Proc. the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 2007, pp.410-420.[62] Meil穉 M. Comparing clusterings: An axiomatic view. In Proc. the 22nd Int. Conf. Machine Learning, Aug. 2005, pp.577584.[63] Rijsbergen C J V. Information Retrieval. Newton, MA, USA: Butterworth-Heinemann, 1979.[64] Milligan G. A Monte Carlo study of thirty internal criterion measures for cluster analysis. Psychometrika, 1981, 46(2): 187-199.[65] Pereira C M M, de Mello R F. A comparison of clustering algorithms for data streams. In Proc. the 1st Int. Conf. Integrated Comp. Tech., May 31-June 2, 2011, pp.59-74.[66] Manning C D, Raghavan P, Schtze H. Introduction to Information Retrieval. New York, NY, USA: Cambridge University Press, 2008.[67] Forestiero A, Pizzuti C, Spezzano G. A single pass algorithm for clustering evolving data streams based on swarm intelligence. Data Mining and Knowledge Discovery, 2013, 26(1): 1-26.[68] Bifet A, Holmes G, Pfahringer B et al. MOA: Massive online analysis, a framework for stream classification and clustering. Journal of Machine Learning Research, 2010, 11: 44-50.[69] Holmes G, Donkin A, Witten I H. WEKA: A machine learning workbench. In Proc. the 2nd Australian and New Zealand Conference on Intelligent Information Systems, Nov. 29Dec. 3, 1994, pp.357-361.[70] Kranen P, Kremer H, Jansen T et al. Clustering performance on evolving data streams: Assessing algorithms and evaluation measures within MOA. In Proc. the IEEE Int. Conf. Data Mining Workshops, Dec. 2010, pp.1400-1403.[71] Kremer H, Kranen P, Jansen T et al. An e甧ctive evaluation measure for clustering on evolving data streams. In Proc. the 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, July 2011, pp.868-876.[72] De Francisci Morales G. SAMOA: A platform for mining big data streams. In Proc. the 22nd Int. Conf. World Wide Web Companion, May 2013, pp.777-778.[73] Tasoulis D K, Ross G, Adams N M. Visualising the cluster structure of data streams. In Proc. the 7th International Conference on Intelligent Data Analysis, Sept. 2007, pp.8192.[74] Ruiz C, Menasalvas E, Spiliopoulou M. C-DenStream: Using domain knowledge on a data stream. In Proc. the 12th International Conference on Discovery Science, Oct. 2009, pp.287-301.[75] Liu L, Jing K, Guo Y et al. A three-step clustering algorithm over an evolving data stream. In Proc. the IEEE Int. Conf. Intelligent Computing and Intelligent Systems, Nov. 2009, pp.160-164.[76] Lin J, Lin H. A density-based clustering over evolving heterogeneous data stream. In Proc. the 2nd Int. Colloquium on Computing, Communication, Control, and Management, Aug. 2009, pp.275-277.[77] Isaksson C, Dunham M, Hahsler M. SOStream: Self organizing density-based clustering over data stream. In Lecture Notes in Computer Science 7376, Perner P (ed.), Springer Berlin Heidelberg, 2012, pp.264-278.[78] Ntoutsi I, Zimek A, Palpanas T et al. Density-based projected clustering over high dimensional data streams. In Proc. the 12th SIAM Int. Conf. Data Mining, April 2012, pp.987-998.[79] Hassani M, Spaus P, Gaber M M, Seidl T. Density-based projected clustering of data streams. In Proc. the 6th Int. Conf. Scalable Uncertainty Management, Sept. 2012, pp.311-324.[80] Jia C, Tan C, Yong A. A grid and density-based clustering algorithm for processing data stream. In Proc. the 2nd Int. Conf. Genetic and Evolutionary Computing, Sept. 2008, pp.517-521.[81] Ren J, Cai B, Hu C. Clustering over data streams based on grid density and index tree. Journal of Convergence Information Technology, 2011, 6(1): 83-93.[82] Yang Y, Liu Z, Zhang J et al. Dynamic density-based clustering algorithm over uncertain data streams. In Proc. the 9th Int. Conf. Fuzzy Systems and Knowledge Discovery, May 2012, pp.2664-2670.[83] Amini A, Teh Ying W. DENGRIS-Stream: A density-grid based clustering algorithm for evolving data streams over sliding window. In Proc. International Conference on Data Mining and Computer Engineering, Dec. 2012, pp.206-210.[84] Bhatnagar V, Kaur S, Chakravarthy S. Clustering data streams using grid-based synopsis. Knowledge and Information Systems, June 2013.[85] Ruiz C, Spiliopoulou M, Menasalvas E. C-DBSCAN: Densitybased clustering with constraints. In Proc. the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, May 2007, pp.216-223.[86] Yang C, Zhou J. HClustream: A novel approach for clustering evolving heterogeneous data stream. In Proc. the 6th IEEE Int. Conf. Data Mining Workshops, Dec. 2006, pp.682-688.[87] Kohonen T. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 1982, 43(1): 59-69.[88] Bohm C, Kailing K, Kriegel H P, Kroger P. Density connected clustering with local subspace preferences. In Proc. the 4th IEEE Int. Conf. Data Mining, Nov. 2004, pp.27-34.[89] Kennedy J F, Kennedy J, Eberhart R C. Swarm Intelligence. Morgan Kaufmann Pub, 2001.[90] Shamshirband S, Anuar N, Kiah M et al. An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Engineering Applications of Artificial Intelligence, 2013, 26(9): 2105-2127.[91] Sander J, Ester M, Kriegel H P, Xu X. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 1998, 2(2): 169-194.[92] Plant C, Teipel S J, Oswald A et al. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. NeuroImage, 2010, 50(1): 162-174.[93] Mete M, Kockara S, Aydin K. Fast density-based lesion detection in dermoscopy images. Computerized Medical Imaging and Graphics, 2011, 35(2): 128-136.[94] Yang D, Rundensteiner E A, Ward M O. Summarization and matching of density-based clusters in streaming environments. Proc. VLDB Endow., 2011, 5(2): 121-132.[95] Lee C H. Mining spatio-temporal information on microblogging streams using a density-based online clustering method. Expert Systems with Applications, 2012, 39(10): 9623-9641.[96] Yu Y, Wang Q, Wang X, Wang H, He J. Online clustering for trajectory data stream of moving objects. Computer Science and Information Systems, 2013, 10(3): 1293-1317. |