Mining Compressing Sequential Patterns
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Toon Calders | Dmitriy Fradkin | Fabian Mörchen | Hoang Thanh Lam | F. Mörchen | Dmitriy Fradkin | T. Calders
[1] P. Grünwald. The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .
[2] Ian H. Witten,et al. Managing gigabytes (2nd ed.): compressing and indexing documents and images , 1999 .
[3] Tijl De Bie,et al. A framework for mining interesting pattern sets , 2010, SIGKDD Explor..
[4] Jilles Vreeken,et al. Finding Good Itemsets by Packing Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[5] D. Huffman. A Method for the Construction of Minimum-Redundancy Codes , 1952 .
[6] James A. Storer,et al. Data compression via textual substitution , 1982, JACM.
[7] Jiawei Han,et al. BIDE: efficient mining of frequent closed sequences , 2004, Proceedings. 20th International Conference on Data Engineering.
[8] Dmitriy Fradkin,et al. Margin-closed frequent sequential pattern mining , 2010, UP '10.
[9] Jilles Vreeken,et al. Krimp: mining itemsets that compress , 2011, Data Mining and Knowledge Discovery.
[10] Paulo J. Azevedo,et al. Time Series Motifs Statistical Significance , 2011, SDM.
[11] Jianyong Wang,et al. Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.
[12] S. Shen-Orr,et al. Network motifs: simple building blocks of complex networks. , 2002, Science.
[13] Tijl De Bie,et al. Maximum entropy models and subjective interestingness: an application to tiles in binary databases , 2010, Data Mining and Knowledge Discovery.
[14] Dmitriy Fradkin,et al. Robust Mining of Time Intervals with Semi-interval Partial Order Patterns , 2010, SDM.
[15] Jilles Vreeken,et al. Making pattern mining useful , 2010, SKDD.
[16] Ian H. Witten,et al. Managing Gigabytes: Compressing and Indexing Documents and Images , 1999 .
[17] Fabian Mörchen,et al. Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression , 2010, Knowledge and Information Systems.
[18] Arno Siebes,et al. StreamKrimp: Detecting Change in Data Streams , 2008, ECML/PKDD.
[19] Christos Faloutsos,et al. Fully automatic cross-associations , 2004, KDD.
[20] Jilles Vreeken,et al. The long and the short of it: summarising event sequences with serial episodes , 2012, KDD.
[21] Ola Svensson,et al. Inapproximability Results for Maximum Edge Biclique, Minimum Linear Arrangement, and Sparsest Cut , 2011, SIAM J. Comput..
[22] Paul M. B. Vitányi,et al. Clustering by compression , 2003, IEEE Transactions on Information Theory.
[23] Li Wei,et al. Compression-based data mining of sequential data , 2007, Data Mining and Knowledge Discovery.
[24] David Haussler,et al. On the Complexity of Iterated Shuffle , 1984, J. Comput. Syst. Sci..
[25] Aristides Gionis,et al. Assessing data mining results via swap randomization , 2007, TKDD.
[26] Gemma C. Garriga,et al. Randomization Techniques for Graphs , 2009, SDM.
[27] Jiawei Han. Mining useful patterns: my evolutionary view , 2010, UP '10.
[28] Lawrence B. Holder,et al. Substucture Discovery in the SUBDUE System , 1994, KDD Workshop.
[29] Toon Calders,et al. Mining Compressing Sequential Patterns , 2012, Stat. Anal. Data Min..
[30] Jilles Vreeken,et al. Slim: Directly Mining Descriptive Patterns , 2012, SDM.
[31] Pauli Miettinen,et al. The Discrete Basis Problem , 2008, IEEE Trans. Knowl. Data Eng..
[32] Fabian Mörchen,et al. Unsupervised pattern mining from symbolic temporal data , 2007, SKDD.
[33] Jilles Vreeken,et al. Identifying the components , 2009, Data Mining and Knowledge Discovery.
[34] Amy Sue Bix. Hard Times in the New Economy , 2004 .
[35] Christos Faloutsos,et al. On data mining, compression, and Kolmogorov complexity , 2007, Data Mining and Knowledge Discovery.
[36] W. J. Conover,et al. Practical Nonparametric Statistics , 1972 .