Data Mining: The Textbook



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ACM CIKM Conference, pp. 811–820, 2007.



  1. L. Akoglu, H. Tong, J. Vreeken, and C. Faloutsos. Fast and reliable anomaly detection in categorical data. ACM CIKM Conference, pp. 415–424, 2012.

BIBLIOGRAPHY

699




  1. R. Albert, and A. L. Barabasi. Statistical mechanics of complex networks. Reviews of modern physics 74, 1, 47, 2002.




  1. R. Albert, and A. L. Barabasi. Topology of evolving networks: local events and uni-versality. Physical review letters 85, 24, pp. 5234–5237, 2000.




  1. P. Allison. Missing data. Sage, 2001.




  1. N. Alon, Y. Matias, and M. Szegedy. The space complexity of approximating the frequency moments. ACM PODS Conference, pp. 20–29, 1996.




  1. S. Altschul, T. Madden, A. Schaffer, J. Zhang, Z. Zhang, W. Miller, and D. Lip-man. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research, 25(17), pp. 3389–3402, 1997.




  1. M. R. Anderberg. Cluster Analysis for Applications. Academic Press, New York, 1973.




  1. P. Andritsos, P. Tsaparas, R. J. Miller, and K. C. Sevcik. LIMBO: Scalable clustering of categorical data. EDBT Conference, pp. 123–146, 2004.




  1. M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Conference, pp. 49–60, 1999.




  1. A. Apostolico, and C. Guerra. The longest common subsequence problem revisited. Algorithmica, 2(1–4), pp. 315–336, 1987.




  1. A. Azran. The rendezvous algorithm: Multiclass semi-supervised learning with markov random walks. International Conference on Machine Learning, pp. 49–56, 2007.




  1. A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh. Clustering with Bregman diver-gences. Journal of Machine Learning Research, 6, pp. 1705–1749, 2005.




  1. S. Basu, A. Banerjee, and R. J. Mooney. Semi-supervised clustering by seeding. ICML Conference, pp. 27–34, 2002.




  1. S. Basu, M. Bilenko, and R. J. Mooney. A probabilistic framework for semi-supervised clustering. ACM KDD Conference, pp. 59–68, 2004.




  1. R. J. Bayardo Jr. Efficiently mining long patterns from databases. ACM SIGMOD, pp. 85–93, 1998.




  1. R. J. Bayardo, and R. Agrawal. Data privacy through optimal k-anonymization. IEEE International Conference on Data Engineering, pp. 217–228, 2005.




  1. R. Beckman, and R. Cook. Outliers. Technometrics, 25(2), pp. 119–149, 1983.




  1. A. Ben-Hur, C. S. Ong, S. Sonnenburg, B. Scholkopf, and G. Ratsch. Support vector machines and kernels for computational biology. PLoS computational biology, 4(10), e1000173, 2008.




  1. M. Benkert, J. Gudmundsson, F. Hubner, and T. Wolle. Reporting flock patterns.



COMGEO, 2008



  1. D. Berndt, and J. Clifford. Using dynamic time warping to find patterns in time series. KDD Workshop, 10(16), pp. 359–370, 1994.

700 BIBLIOGRAPHY





  1. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is “nearest neighbor” meaningful? International Conference on Database Theory, pp. 217–235, 1999.




  1. V. Barnett, and T. Lewis. Outliers in statistical data. Wiley, 1994.




  1. M. Belkin, and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. NIPS, pp. 585–591, 2001.




  1. M. Bezzi, S. De Capitani di Vimercati, S. Foresti, G. Livraga, P. Samarati, and R. Sassi. Modeling and preventing inferences from sensitive value distributions in data release. Journal of Computer Security, 20(4), pp. 393–436, 2012.




  1. L. Bergroth, H. Hakonen, and T. Raita. A survey of longest common subsequence algorithms. String Processing and Information Retrieval, 2000.




  1. S. Bhagat, G. Cormode, and S. Muthukrishnan. Node classification in social networks.




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