Related Work in Machine Learning

CFP: Data Mining, Analytics, Big Data, Data Science, and Knowledge Discovery

Books

• Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman
• A Course in Machine Learning, Hal Daumé III
• Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman
• Pattern Recognition and Machine Learning, C. Bishop

Conferences (Conferences in CS)

• Intl. Conf. on Machine Learning (ICML) [CFP: ~Jan]
• ACM Intl. Conf. on Knowledge Discovery and Data Mining (KDD) [CFP: ~Mar]
• IEEE Intl. Conf. on Data Mining (ICDM) [CFP: ~May]
• SIAM Intl. Conf. on Data Mining (SDM) [CFP: ~Oct]
• Neural Information Processing Systems (NIPS) [CFP: ~Jul]
• National Conf. AI (AAAI) and Innovative Applications of AI (IAAI) [CFP: ~Sep]
• Intl. Joint Conf. on AI (IJCAI) [CFP: ~Jan]
• Conference on Computational Learning Theory (COLT) [CFP: ~Jan]
• European Conference on Machine Learning (ECML) [CFP: ~Mar]
• Genetic and Evolutionary Computation Conference (GECCO) [CFP: ~Apr]
• Inductive Logic Programming (ILP) [CFP: ~Jan]
• Florida AI Research Society (FLAIRS) [CFP: ~Oct]

Journals (Journals in CS)

• Machine Learning Journal (MLJ)
• Journal of Machine Learning Research (JMLR)
• Data Mining and Knowledge Discovery (DMKD)
• ACM Trans. Knolwedge Discovery from Data (TKDD)
• Artificial Intelligence (AIJ)
• Journal of Artificial Intelligence Research (JAIR)
• IEEE Transactions on Knowledge and Data Engineering (TKDE)

Projects

• IBM KDD Groups
• QUEST, IBM Almaden
• Data Abstraction, IBM Watson
• UC, Irvine
• ML dept, CMU

Papers

Decisions Trees

• Decision Tree Induction Based on Efficient Tree Restructuring P. Utgoff, N. Berkman & J. Clouse. Machine Learning, 29(1):5-44, 1997.

Rules

• Learning Decision Rules by Randomized Iterative Local Search, Chisholm, M. and Tadepalli, P. ICML, 2002.
• Lightweight Rule Induction S. Weiss & N. Indurkhya. ICML, p.1135-42. 2000.
• A Simple, Fast, and Effective Rule Learner W. Cohen & Y. Singer, Proc. AAAI, 1999.
• Beyond Market Baskets: Generalizing Association Rules to Dependence Rules, C. Silverstein, S. Brin & R. Motwani, Data Mining and Knowledge Discovery, January 1998, pp. 39-68.
• Fast Effective Rule Induction W. Cohen. Proc. ICML, 1995.
• Fast Algorithms for Mining Association Rules, R. Agrawal & R. Srikant Proc. VLDB, 1994.
• Mining Associations between Sets of Items in Massive Databases R. Agrawal, T. Imielinski and A. Swami. Proc. SIGMOD, p207-216, 1993.
• The CN2 Induction Algorithm, P. Clark & T. Niblett. Machine Learning, 3(4), p261-283, 1989.

Clustering

• TraClass: Trajectory Classification Using Hierarchical Region-Based and Trajectory-Based Clustering. J. Lee, J. Han, X. Li, H. Gonzalez. VLDB, pp 140-149, 2008.
• Graph-Based Hierarchical Conceptual Clustering, I. Jonyer, L. Holder & D. Cook, J. Machine Learning Research, 2:19-43, 2001.
• LOF: Identifying Density-Based Local Outliers, M. Breunig, H. Kriegel, R. Ng & J. Sander, SIGMOD, pp. 93-104, 2000.
• ROCK: A Robust Clustering Algorithm for Categorical Attributes, S. Guha, R. Rastoqi & K Shim, Information Systems, 25(5):345-366, 2000.
• Learning to match and cluster large high-dimensional data sets for data integration, W. Cohen & J. Richman, KDD, 2002.
• CHAMELEON: Hierarchical Clustering Algorithm using Dynamic Modeling G. Karypis, E. Han & V. Kumar. IEEE Computer, 1999.
• Clustering categorical data: An approach based on dynamical systems, D. Gibson, J. Kleinberg & P. Raghavan, Proc. VLDB, 1998.
• An Efficient Approach to Clustering in Large Multimedia Databases with Noise, A. Hinneburg & D. Kelm. Proc. KDD, 1998.

Probabilistic Models

• Probabilistic Models of Relational Structure, L. Getoor, N. Friedman, D. Koller & B. Taskar. ICML, 2001.

Learning Formal Languages, Automata

• MDL-Based Context-Free Graph Grammar Induction, I. Jonyer, L. Holder & D. Cook, Proc. FLAIRS, 2003.
• Learning context-free grammars with a simplicity bias, P. Langley & S. Stromsten, Proc. ECML (pp. 220-228) 2000.

Cost-sensitive and imbalance class distribution

• A Fully Distributed Framework for Cost-Sensitive Data Mining, W. Fan, h. Wang. P. Yu, S. Stolfo, Proc. ICDCS, p. 445-446, 2002.
• Reducing multiclass to binary by coupling probability estimates, B. Zadrozny, Advances in Neural Information Processing Systems 14 (NIPS*2001), 2001.
• Learning and Making Decisions When Costs and Probabilities are Both Unknown, B. Zadrozny & C. Elkan, Proc. of the Seventh Intl. Conf. on Knowledge Discovery and Data Mining, 2001.
• Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, C. Elkan, Proceedings of the Eighteenth Intl. Conf. on Machine Learning , 2001.
• The Foundations of Cost-Sensitive Learning, C. Elkan, Proc. of the Seventeenth Intl. Joint Conf. on Artificial Intelligence, 2001.

Outlier/Anomaly Detection

• Trajectory Outlier Detection: A Partition-and-Detect Framework. J. Lee, J. Han, and X. Li, ICDE, 2008.
• ROAM: Rule- and Motif-Based Anomaly Detection in Massive Moving Object Data Sets. X. li, J. Han, S. Kim & H. Gonzalez. SIAM Intl. Conf. Data Mining (SDM), 2007.
• Neighborhood based detection of anomalies in high dimensional spatio-temproal sensor datasets. N. Adam, V. Janeja & V. Atluri. ACM Symp. on Applied Computing (SAC), pp. 576-583, 2004.
• Outlier Detection for High Dimensional Data, C. Aggarwal & P. Yu, SIGMOD, 2001.
• LOF: Identifying Density-Based Local Outliers, M. Breunig, H. Kriegel, R. Ng & J. Sander, SIGMOD, pp. 93-104, 2000.

ML and Natural Language Processing

• Grounded Spoken Language Acquisition: Experiments in Word Learning D. Roy, IEEE Transactions on Multimedia, 2003 (in press).
• An unsupervised algorithm for segmenting categorical timeseries into episodes P. Cohen and B. Heeringa and N. Adams, Proc. IEEE Intl Conf. Data Mining, 2002.
• Learning Words and Syntax for a Visual Description Task D. Roy, Computer Speech and Language, 16(3), 2002.
• Learning Visually Grounded Words and Syntax of Natural Spoken Language D. Roy, Evolution of Communication 4(1), p33-56, 2000/01.
• An efficient, probabilistically sound algorithm for segmentation and word discovery, M. Brent. Machine Learning, 34(1):71-106, 1999.
• Speech segmentation and word discovery: A computational perspective, M. Brent. Trends in Cognitive Science, 3:294-301, 1999.
• Similarity-Based Models of Word Cooccurrence Probabilities, I. Dagan, L. Lee & F. Pereira, Machine Learning, 34(1):43-69, 1999.
• A maximum entropy approach to natural language processing A. Berger, S. Pietra, and V. Pietra, Computational Linguistics, 22(1), 1996.
• Good bigrams, C. Johansson, In Proceedings of COLING-96, pages 592--597, 1996.
• Parsing a Natural Language Using Mutual Information Statistics D. Magerman & M. Marcus, AAAI, p984-989, 1990.

ML and Time Series (Temporal Data)

• Estimating the number of segments in time series data using permutation tests, K. Vasko & H. Toivonen, ICDM, 2002.
• An Online Algorithm for Segmenting Time Series, E. Keogh, S. Chu, D. Hart, & M. Pazzani. IEEE Intl. Conf. Data Mining, p289-296, 2001.
• Event Detection from Time Series Data V. Guralnik & K. Srivastava KDD, 1999.
• Clustering Time Series with Hidden Markov Models and Dynamic Time Warping, T. Oates, L. Firoiu & P. Cohen. IJCAI Workshop on Sequence Learning, 1999.
• Minimum Message Length Segmentation, J. Oliver. PAKDD, p222-233, 1998.
• Learning to Classify Sensor Data, S. Manganaris. IJCAI Workshhop on Machine Learning in Engineering, 1995.
• Learning Time Series for Intelligent Monitoring S. Manganaris & D. Fisher. Third Intl. Symp. on AI, Robotics, and Automation for Space p71-74, 1994.

Process models

• Discovering ecosystem models from time-series data, George, D., Saito, K., Langley, P., Bay, S., & Arrigo, K, Proceedings of the Sixth Intl. Conf. on Discovery Science.
• Robust induction of process models from time-series data., Langley, P., George, D., Bay, S., & Saito, K. , Proceedings of the Twentieth Intl. Conf. on Machine Learning (pp. 432-439), 2003.

Statistics

• Selecting the Right Interestingness Measure for Association Patterns, P. Tan, V. Kumar & J. Srivastava, Proc of the Eighth ACM SIGKDD (KDD-2002)
• Zipf, Power-laws, and Pareto - a ranking tutorial, L. Adamic, Xerox PARC, 2000.
• Interestingness Measures for Association Patterns, P. Tan & V. Kumar, KDD 2000 Workshop on Postprocessing in Machine Learning and Data Mining.
• Efficient Bayesian Parameter Estimation in Large Discrete Domains, N. Friedman & Y. Singer, NIPS, 1999.
• An empirical study of smoothing techniques for language modeling S. Chen & J. Goodman, Proceedings of the 34th Meeting of the Association for Computational Linguistics, pp 310--31, 1996. TR version: TR-10-98, Computer Science Group, Harvard University, 1998.
• Good-Turing Smoothing Without Tears W. Gale, 1994. [W. Gale & G. Sampson, Good-Turing frequency estimation without tears, Journal of Quantitative Linguistics 2:217-37, 1995]
• What's Wrong with Adding One?, W. Gale and K. Church, In N. Oostdijk and P. de Haan (eds.), Corpus-Based Research into Language: In honour of Jan Aarts, Rodopi, Amsterdam, pp. 189-200, 1994.
• The zero frequency problem: estimating the probabilities of novel events in adaptive text compression I. Witten and T. Bell, CS Tech Report: 1989-347-09, Univ. of Calgary, 1989. [also in IEEE Trans. on Info. Theory, 37(4):1085-1094, 1991]

Information Theory

• A mathematical theory of communication, C. Shannon, Bell System Technical Journal, vol. 27, pp. 379-423 and 623-656, July and October, 1948.