Data Mining
Minería de Datos

Introducción a la Minería de Datos

Introducción al Data Mining

Lecturas recomendadas

• Usama Fayyad, Gregory Piatetsky-Shapiro & Padhraic Smyth: The KDD process for extracting useful knowledge from volumes of data, Communications of the ACM, November 1996, pp. 27-34. DOI 10.1145/240455.240464

Publicaciones de IDBIS

• Fernando Berzal, Ignacio Blanco, Juan Carlos Cubero & Nicolás Marín: Component-based Data Mining Frameworks. Communications of the ACM, Vol. 45, No. 12, December 2002, pp. 97-100. DOI 10.1145/585597.585624
• Fernando Berzal, Juan Carlos Cubero, Nicolás Marín, José María Serrano & Ignacio Blanco: Usability Issues in Data Mining Systems. ICEIS 2003, Proceedings of the 5th International Conference on Enterprise Information Systems, Angers, France, April 22-26, 2003. Volume II - Artificial Intelligence and Decision Support Systems, pp. 418-421
• Fernando Berzal, Juan Carlos Cubero & Aída Jiménez: Fine-grained Performance Evaluation and Monitoring Using Aspects. ICSOFT 2008, Proceedings of the 3rd International Conference on Software and Data Technologies, Porto, Portugal, July 5-8, 2008.
• Fernando Berzal, Juan Carlos Cubero & Aída Jiménez: The Design and Use of the TMiner Component-based Data Mining Framework. Expert Systems with Applications, Volume 36, Issue 4, May 2009, pp. 7882-7887. DOI 10.1016/j.eswa.2008.11.033

Reglas de asociación y patrones frecuentes

Reglas de asociación

Patrones secuenciales (análisis de secuencias)

Patrones en árboles (análisis de estructuras de datos no lineales)

Patrones en redes (subgrafos frecuentes & motif discovery)

Lecturas recomendadas

• Rakesh Agrawal & Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules. VLDB 1994, pages 487-499.
• Jiawei Han, Jian Pei & Yiwen Yin: Mining Frequent Patterns without Candidate Generation, SIGMOD 2000, pages 1-12 (ACM SIGMOD Record, Volume 29, Issue 2, June 2000). DOI 10.1145/342009.335372

Surveys

• Jochen Hipp, Ulrich Güntzer & Gholamreza Nakhaeizadeh: Algorithms for Association Rule Mining - A General Survey and Comparison. SIGKDD Explorations Newsletter, Vol. 2, No. 1, pages 58-64, June 2000. DOI 10.1145/360402.360421
• Aaron Ceglar & John F. Roddick: Association Mining. ACM Computing Surveys, Vol. 38, No. 2, July 2006. DOI 10.1145/1132956.1132958
• Liqiang Geng & Howard J. Hamilton: Interestingness Measures for Data Mining: A Survey. ACM Computing Surveys, Vol. 38, No. 3, September 2006. DOI 10.1145/1132960.1132963

Publicaciones de IDBIS

• Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & José María Serrano: TBAR: An efficient method for association rule mining in relational databases, Data & Knowledge Engineering, Vol. 37, No. 1, April 2001, pp. 47-64. DOI 10.1016/S0169-023X(00)00055-0
• Fernando Berzal, Ignacio Blanco, Daniel Sánchez & María-Amparo Vila: Measuring the accuracy and interest of association rules: A new framework, Intelligent Data Analysis, Volume 6, Number 3/2002, Pages 221-235, IOS Press, September 2002
• Marco-Antonio Balderas, Fernando Berzal, Juan Carlos Cubero, Eduardo Eisman & Nicolás Marín: Discovering Hidden Association Rules. KDD 2005 Workshop on Data Mining Methods for Anomaly Detection, Chicago, IL, August 21st, 2005
• Aída Jiménez, Fernando Berzal & Juan Carlos Cubero: Interestingness Measures for Association Rules within Groups, Intelligent Data Analysis, Volume 17, Issue 2, 2013, pages 195-215. DOI 10.3233/IDA-130574

Clasificación y predicción

Clasificación

Predicción de series temporales

Predicción de enlaces

Lecturas recomendadas

• Sreerama K. Murthy: Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery 2(4):345-389 (1998). DOI 10.1023/A:1009744630224
• Tjen-Sien Lim, Wei-Yin Loh, Yu-Shan Shih: A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning 40(3):203-228 (2000). DOI 10.1023/A:1007608224229
• Janez Demsar: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7: 1-30 (2006)
• David J. Hand: Classifier Technology and the Illusion of Progress. Statistical Science 21(1):1-14 (2006). DOI 10.1214/088342306000000060

Publicaciones de IDBIS

• Fernando Berzal, Juan Carlos Cubero, Daniel Sánchez & José María Serrano: ART: A Hybrid Classification Model. Machine Learning, Volume 54, Number 1, Pages 67-92, January 2004. DOI B:MACH.0000008085.22487.a6
• Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & Daniel Sánchez: Building multi-way decision trees with numerical attributes. Information Sciences, Volume 165, pages 73-90, 2004. DOI 10.1016/j.ins.2003.09.018
• Fernando Berzal, Juan Carlos Cubero, Fernando Cuenca & María José Martín-Bautista: On the quest for easy-to-understand splitting rules. Data & Knowledge Engineering, Vol. 44, No. 1, January 2003, pp. 31-48. DOI 10.1016/S0169-023X(02)00062-9
• Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & José Luis Polo: An overview of alternative rule evaluation criteria and their use in separate-and-conquer classifiers. ISMIS 2006, Foundations of Intelligent Systems, LNAI 4203, pp. 591-600. DOI 10.1007/11875604_66
• José Luis Polo, Fernando Berzal & Juan Carlos Cubero: Taking class importance into account. ICHIT 2006, International Conference on Hybrid Information Technology, Cheju Island, Korea, November 9-11, 2006. DOI 10.1109/ICHIT.2006.233
• José Luis Polo, Fernando Berzal & Juan Carlos Cubero: Class-Oriented Reduction of Decision Tree Complexity. ISMIS 2008, Foundations of Intelligent Systems, LNAI 4994, pp. 48-57. DOI 10.1007/978-3-540-68123-6_5

Clustering

Clustering

Por familias de técnicas

Métodos de agrupamiento por particiones

Métodos de agrupamiento jerárquico

Métodos basados en densidad

Clustering en subespacios [subspace clustering]

En redes...

Detección de comunidades

Lecturas recomendadas

• Martin Ester, Hans-Peter Kriegel, Jörg Sander & Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 1996, pages 226-231.
• Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos & Prabhakar Raghavan: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. SIGMOD 1998, pages 94-105.
• George Karypis, Eui-Hong (Sam) Han & Vipin Kumar: Chameleon: Hierarchical Clustering Using Dynamic Modeling, Computer, Vol. 32, No. 8, August 1999, pp. 68-75
• Levent Ertöz, Michael Steinbach & Vipin Kumar: Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data. Third SIAM International Conference on Data Mining, San Francisco, CA, USA, May 1-3. SDM 2003

Surveys

• Anil K. Jain, M. Narasimha Murty & Patrick J. Flynn: Data Clustering: A Review. ACM Computing Surveys, Vol. 31, No. 3, pages 264-323, September 1999. DOI 10.1145/331499.331504
• Lance Parsons, Ehtesham Haque & Huan Liu: Subspace Clustering for High Dimensional Data: A Review. ACM SIGKDD Explorations Newsletter, Volume 6, Issue 1, June 2004, pages 90-105. DOI 10.1145/1007730.1007731
• Huan Liu, Farhad Hussain, Chew Lim Tan & Manoranjan Dash: Discretization: An Enabling Technique. Data Mining and Knowledge Discovery 6(4):393-423 (2002). DOI 10.1023/A:1016304305535

Publicaciones de IDBIS

• Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & Daniel Sánchez: Building multi-way decision trees with numerical attributes. Information Sciences, Volume 165, pages 73-90, 2004. DOI 10.1016/j.ins.2003.09.018

Anomalías

Detección de anomalías

Reglas de asociación anómalas

Lecturas recomendadas

• Stefan Axelsson: The Base-Rate Fallacy and its Implications for the Difficulty of Intrusion Detection. RAID'99, 2nd International Workshop on the Recent Advances in Intrusion Detection, West Lafayette, Indiana, USA, September 7-9, 1999.
• Farhad Hussain, Huan Liu, Einoshin Suzuki & Hongjun Lu: Exception Rule Mining with a Relative Interestingness Measure. PAKDD'2000, 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 1805, pages 86-97.
• Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng & Jörg Sander: LOF: identifying density-based local outliers. SIGMOD'2000, International Conference on Management of Data, Dallas, Texas, United States (also in ACM SIGMOD Record, Volume 29, Issue 2, June 2000, pages 93-104).

Surveys

• Aleksandar Lazarevic, Aysel Ozgur, Levent Ertöz, Jaideep Srivastava, Vipin Kumar: A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. SDM'2003, Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, May 1-3, 2003.
• Markos Markou & Sameer Singh: Novelty Detection: A Review - Part 1: Statistical Approaches. Signal Processing, Volume 83, Issue 12, December 2003, pages 2481-2497. DOI 10.1016/j.sigpro.2003.07.018

Publicaciones de IDBIS

• Marco-Antonio Balderas, Fernando Berzal, Juan Carlos Cubero, Eduardo Eisman & Nicolás Marín: Discovering Hidden Association Rules. KDD 2005 Workshop on Data Mining Methods for Anomaly Detection, Chicago, IL, August 21st, 2005

Graph Mining

Redes

Patrones en redes

Predicción de enlaces

Detección de comunidades

Lecturas recomendadas

• Xifeng Yan & Jiawei Han: gSpan: Graph-Based Substructure Pattern Mining. ICDM 2002, pages 721-724
• Michihiro Kuramochi & George Karypis: An Efficient Algorithm for Discovering Frequent Subgraphs. IEEE TKDE 16(9):1038-1051 (2004)
• Ehud Gudes, Solomon Eyal Shimony & Natalia Vanetik: Discovering Frequent Graph Patterns Using Disjoint Paths. IEEE TKDE 18(11):1441-1456 (2006)
• Mohammed J. Zaki: Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications. IEEE TKDE 17(8):1021-1035 (2005)

• Duncan J. Watts & Steven H. Strogatz: Collective dynamics of 'small-world' networks. Nature, 393:440-442 (1998)
• Albert-László Barabási & Réka Albert: Emergence of scaling in random networks. Science, 286:509-512 (1999)
• Steven H. Strogatz: Exploring Complex Networks. Nature, 410:268-275 (2001)
• Mark Newman: The structure and function of complex networks. SIAM Review 45:167-256 (2003)
• Jure Leskovec, Jon M. Kleinberg & Christos Faloutsos: Graphs over time: densification laws, shrinking diameters and possible explanations. KDD'2005

Publicaciones de IDBIS

• Víctor Martínez, Fernando Berzal & Juan-Carlos Cubero: Adaptive degree penalization for link prediction. Journal of Computational Science, Volume 13, March 2016, pages 1-9. DOI 10.1016/j.jocs.2015.12.003
• Aída Jiménez, Fernando Berzal & Juan-Carlos Cubero: Frequent Tree Pattern Mining: A Survey. Intelligent Data Analysis, Volume 14(6), November 2010, pages 603-622. DOI 10.3233/IDA-2010-0443
• Aída Jiménez, Fernando Berzal & Juan-Carlos Cubero: POTMiner: Mining Ordered, Unordered, and Partially-Ordered Trees. Knowledge and Information Systems, vol. 23, no. 2, May 2010, pp. 199-224. DOI 10.1007/s10115-009-0213-3

• Víctor Martínez, Fernando Berzal & Juan-Carlos: The NOESIS Open Source Framework for Network Data Mining. Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR 2015), Lisbon, Portugal, November 12-14, 2015, pages 316-321. DOI 10.5220/0005610103160321

• Fernando Berzal, Juan Carlos Cubero & Aída Jiménez: Hierarchical program representation for program element matching. IDEAL 2007, 8th International Conference on Intelligent Data Engineering and Automated Learning, LNCS 4881, pp. 467–476. DOI 10.1007/978-3-540-77226-2_48
• Aída Jiménez, Miguel Molina-Solana, Fernando Berzal & Waldo Fajardo: Mining transposed motifs in music. Journal of Intelligent Information Systems, vol. 36, no. 1, 2011, pages 99-115. DOI: DOI 10.1007/s10844-010-0122-7
• Aída Jiménez, Fernando Berzal & Juan-Carlos Cubero: Using trees to mine multirelational databases. Data Mining and Knowledge Discovery, January 2012, Volume 24, Issue 1, pp 1-39. DOI 10.1007/s10618-011-0218-x

Apéndice: Herramientas Estadísticas

Análisis Estadístico con SPSS. Inferencia estadística. Modelos paramétricos

Bibliografía

Libros de texto

Pang-Ning Tan, Michael Steinbach & Vipin Kumar: Introduction to Data Mining Addison-Wesley, 2006. ISBN 0321321367

Jiawei Han & Micheline Kamber: Data Mining: Concepts and Techniques Morgan Kaufmann, 2006 ISBN 1558609016

Revistas de investigación

• ACM Transactions on Knowledge Discovery from Data (TKDD)
• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• Data Mining and Knowledge Discovery
• SIGKDD Explorations
• Data & Knowledge Engineering (DKE)
• Knowledge and Information Systems (KAIS)
• Machine Learning

Congresos

• KDD: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
• ICDM: IEEE International Conference on Data Mining
• PKDD: European Conference on Principles of Data Mining and Knowledge Discovery
• SDM: SIAM International Conference on Data Mining
• ICDE: International Conference on Data Engineering
• CIKM: ACM International Conference on Information and Knowledge Management
• ADMA: International Conference on Advanced Data Mining and Applications
• PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining
• MLDM: International Conference on Machine Learning and Data Mining
• DS: International Conference on Discovery Science
• ICDM: Industrial Conference on Data Mining

• SIGMOD: ACM SIGMOD International Conference on Management of Data
• PODS: ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems
• VLDB: International Conference on Very Large Data Bases
• EDBT: International Conference on Extending Database Technology
• SSDBM: International Conference on Scientific and Statistical Database Management
• DEXA: Database and Expert Systems Applications
• DASFAA: Database Systems for Advanced Applications

• AAAI: AAAI Conference on Artificial Intelligence
• ECML: European Conference on Machine Learning
• ICML: International Conference on Machine Learning
• IDA: International Symposium on Intelligent Data Analysis
• IDEAL: International Conference on Intelligent Data Engineering and Automated Learning
• ISMIS: International Syposium on Methodologies for Intelligent Systems
• WI: IEEE / WIC / ACM International Conference on Web Intelligence