Εξόρυξη Δεδομένων και Αλγόριθμοι Μάθησης

Λίστα papers

  1. J. Han, J. Pei, Y, Yin, R. Mao, Mining Frequent Patterns without Candidate Generation:A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, 8(1), 2004, 53-87.

Taneli Mielikäinen, Frequency-based views to pattern collections, Discrete Applied Mathematics, v.154 n.7, p.1113-1139, 1 May 2006

  1. Gosta Grahne , Jianfei Zhu, Fast Algorithms for Frequent Itemset Mining Using FP-Trees, IEEE Transactions on Knowledge and Data Engineering, v.17 n.10, p.1347-1362, October 2005

Son N. Nguyen , Maria E. Orlowska, A further study in the data partitioning approach for frequent itemsets mining, Proceedings of the 17th Australasian Database Conference, p.31-37, January 16-19, 2006, Hobart, Australia

  1. Eirinaki, Vazirgiannis, Web Mining for Web Personalization, ACM Trnasactions on Internet Technology (TOITT), 3(1), 2003

Gunduz, and Ozsu, 2003, A web page prediction model based on click-stream tree representation of user behavior, In Proceedings of the 9th ACM SIGKDD International Conference on Knoweledge Discovery and Data Mining KDD 2003.

  1. Chen, LaPaugh, Singh, 2002, Predicting Category Accesses for a user in a structured information space, In Proc. Of the 25th Annual Int. ACM SIGIR Conf. on Research nd Development in information Retrieal 2002.

Pei, Han, Mortazavi, Pinto, Chen, Dayal, Hsu, Mining sequential patterns by pattern growth: the PrefixSpan approach, IEEE Transactions on Knowledeg and Data Engineering, 16,11 (2004), 1424-1440.

  1. Liu, Hsu, Ma, Mining Association Rules with Multiple Minimum Supports, ACM SIGKDD, 1999.

Ming-Cheng Tseng , Wen-Yang Lin, Efficient mining of generalized association rules with non-uniform minimum support, Data & Knowledge Engineering, v.62 n.1, p.41-64, July, 2007

6.HyunyoonYun , DanshimHa , BuhyunHwang , KeunHoRyu, Miningassociationrulesonsignificantraredatausingrelativesupport, JournalofSystemsandSoftware, v.67 n.3, p.181-191, 15 September 2003

KrishnaGade, Jianyong Wang , George Karypis, Efficient closed pattern miningin the presence of tough block constraints, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA

7.B.C.M. Fung, K. Wang, Martin Ester, Hierarchical Document Clustering Using Frequent Itemsets, In Proceedings of the 3rd SIAM International Conference on Data Mining, 2003.

K. Wang, C. Xu, B. Liu, Clustering Transactions Using Large Items, In Proceedings of the 8th ACM International Conference on Information and Knowledge Management, pp.483-490, 1999.

8.Wenyuan Li, Wee-Keong Ng,Ying Liu and Kok-Leong Ong., Enhancing the Effectiveness of Clustering with Spectra Analysis, IEEE Transactions on Knowledge and Data Engineering, 2007. Vol. 17, No. 7, pp. 887-902.

Cheng-Ru Lin, Ken-Hao Liu, Ming-Syan Chen, Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains, IEEE Transactions on Knowledge and Data Engineering, 2005. Vol. 17, no.5, pp. 628-637.

  1. WalterA.Kosters, ElenaMarchiori and ArdA.J.Oerlemans, Mining Clusters with Association Rules, Advances in Intelligent Data Analysis, 1999.

Zhuang, Dai, A maximal frequent itemset approach for web document clustering, Proceedingsof CIT 2004

10.R. Agrawal, T. Imielinski, A. Swami: Mining Associations between Sets of Items in Massive Databases, Proc. of the ACM SIGMOD Int'l Conference on Management of Data, WashingtonD.C., May 1993, 207-216.

H. Toivonen, Sampling Large Databases for Association Rules, VLDB 1996, pp. 134-145.

11.G. Cooper and E. Herskovits, A Bayesian Method for the Induction of Probabilistic Networks from Data, Machine Learning 9 (1992) pp. 309-347 .

J. Gehrke, V. Ganti, R. Ramakrishnan, and W.-Y. Loh, BOAT --- Optimistic Decision-Tree Construction,' 1999 SIGMOD pp. 169-180.

12. R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, A. Swami: An Interval Classifier for Database Mining Applications', in Proceeding of the VLDB Conference, Vancouver, BC, Canada, 1992, pp.560-573.

M. Mehta, R. AgrawalandJ. Rissanen, "SLIQ: AFastScalableClassifierforDataMining", inProceedingsofthe 5thInternational Conference on Extending Database Technology, Avignon, France, Mar. 1996.

13.When is 'Nearest Neighbor' meaningful?, K. S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, ICDT 1999, pp. 217--235.

S. Guha, R. Rastogi, and K. Shim, CURE: An Efficient Clustering Algorithm for Large Databases, SIGMOD 1998.

14. S. Basu, M. Bilenko, R. Mooney, A Probabilistic Framework for Semi-Supervised Clustering, Proceedings of the 10th ACM SIGKDD 2004, pp. 59-68, Seattle, WA, Aug. 2004.

Christos Faloutsos, M. Ranganathan and Yannis Manolopoulos, Fast subsequence matching in time-series databases, SIGMOD, 1994, pp. 419-429.

15.V. Megalooikonomou, Q. Wang, G. Li, C. Faloutsos, A Multiresolution Symbolic Representation of Time Series, Proceedings of the 21st IEEE International Conference on Data Engineering (ICDE05), Tokyo, Japan, April 5-8, 2005, pp. 668-679.

L. J. Latecki, V. Megalooikonomou, Q. Wang, R. Lakaemper, C. A. Ratanamahatana, and E. Keogh, Elastic Partial Matching of Time Series, Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'05), Porto, Portugal, Lecture Notes in Computer Science, Vol. 3721, pp. 577-584, 2005.

16.Data-streams and histograms, Sudipto Guha, Nick Koudas and Kyuseok Shim, ACM Symposium on Theory of Computing, pages 471-475, 2001.

S. Brin, Extracting Patterns and Relations from the World-Wide Web,

17.S. Brin and L. Page, The Anatomy of a Large-Scale Hypertextual Web Search Engine, WWW7/Computer Networks (1-7), 1998, pp. 107-117.

A. Bhattacharya, V. Ljosa, J.-Y. Pan, M. Verardo, H. Yang, C. Faloutsos and A. Singh, ViVo: Visual Vocabulary Construction for Mining Biomedical Images ICDM, Houston, TX, U.S.A., November 27-30, 2005.

18.Shivnath Babu, Minos Garofalakis, Rajeev Rastogi,SPARTAN: A Model-Based Semantic Compression System for Massive Data Tables, ACM SIGMOD, May 2001, Santa Barbara, CA, pp. 283-295.

J. Leskovec, J. Kleinberg and C. Faloutsos, Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations, KDD 2005, Chicago, IL.

19.S. Papadimitriou, A. Gionis, P. Tsaparas, R.A. Vaisanen, H. Mannila, C. Faloutsos, Parameter-Free Spatial Data Mining Using MDL, ICDM, Houston, TX, U.S.A., November 27-30, 2005.

C. Faloutsos and V. Megalooikonomou, On Data Mining, Compression, and Kolmogorov Complexity,Data Mining and Knowledge Discovery , Tenth Anniversary Issue, 2007.