Data Mining

14 Apr 2013 16:45

I've taught a course on this, so I ought to be able to describe it, oughtn't I? Data mining, more stuffily "knowledge discovery in databases", is the art of finding and extracting useful patterns in very large collections of data. It's not quite the same as machine learning, because, while it certainly uses ML techniques, the aim is to directly guide action (praxis!), rather than to develop a technology and theory of induction. In some ways, in fact, it's closer to what statistics calls "exploratory data analysis", though with certain advantages and limitations that come from having really big data to explore.

Kernel methods probably deserve their own notebook.

Leo Breiman, "Statistical Modeling: The Two Cultures", Statistical Science 16 (2001): 199--231 [very much including the discussion by others and the reply by Breiman]
Pedro Domingos, "A Few Useful Things to Know about Machine Learning"
David Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining [The textbook I teach from; also a book I learned a lot from.]
Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age [Precis as a 43 pp. PDF working paper]
Trever Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction [Website, with full text free in PDF]
Sholom M. Weiss and Nitin Indrukyha, Predictive Data Mining: A Practical Guide [Pedestrian, but it is practical, and adapted to the meanest, i.e. the managerial, understanding]

Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján, "Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection", Journal of Machine Learning Research 13 (2012): 27--66
Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, and Duncan J. Watts, "Predicting consumer behavior with Web search", Proceedings of the National Academy of Sciences (USA) 107 (2010): 17486--17490 [A case study in using data mining, while recognizing limitations]
Aleks Jakulin and Ivan Bratko, "Quantifying and Visualizing Attribute Interactions", cs.AI/0308002
Jon Kleinberg, Christos Papadimitriou and Prabhakar Raghavan, "A Microeconomic View of Data Mining", Data Mining and Knowledge Discovery 2 (1998) [PDF]
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan, "A Scalable Bootstrap for Massive Data", arxiv:1112.5016
Kling, Scherson and Allen, "Parallel Computing and Information Capitalism," in Metropolis and Rota (eds.), A New Era in Computation (1992) [A batch of UC Irvine comp. sci. professors who write like sociologists. " `Information capitalism' refers to forms of organization in which data-intensive techniques and computerization are key strategic resources for corporate production."]
Erik Larson, The Naked Consumer: How Our Private Lives Become Public Commodities
R. Dean Malmgren, Jake M. Hofman, Luis A. N. Amaral, Duncan J. Watts, "Characterizing Individual Communication Patterns", arxiv:0905.0106
Yong Wang, Ilze Ziedins, Mark Holmes, Neal Challands, "Tree Models for Difference and Change Detection in a Complex Environment", Annals of Applied Statistics 6 (2012): 1162--1184, arxiv:1202.1561 [In an ordinary classification tree, we are interested in the distribution of the class labels $ Y $ given the predictors $ X $, i.e., $ \Pr(Y|X) $, and make splits on $ X $ so that (in essence) the conditional entropy $ H[Y|X] $ becomes small. This is of course equivalent to making splits so that the divergence of $ Pr(Y|X) $ from $ Pr(Y) $ is maximized. What they are interested in is not classification but describing how the different classes are distinct, so the relevant distribution is $ Pr(X|Y) $, and they want a big divergence between $ Pr(X) $ and $ Pr(X|Y) $.]

My lecture notes for my data mining class [However, many of them are based on lecture notes originally written by Tom Minka, and modesty does not forbid me from recommending his work.]

Arvind Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian, "A Unified Algorithmic Framework for Multi-Dimensional Scaling", arxiv:1003.0529
Dhoha Almazro, Ghadeer Shahatah, Lamia Albdulkarim, Mona Kherees, Romy Martinez, William Nzoukou, "A Survey Paper on Recommender Systems", arxiv:1006.5278
Ian Ayres, Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart [Despite the painful title, Ayres has done cool applied work in social statistics]
David L. Banks and Yasmin H. Said, "Data Mining in Electronic Commerce", Statistical Science 21 (2006): 234--246, math.ST/0609204
Gérard Biau, Benoît Cadre, Laurent Rouvière, "Statistical analysis of $k$-nearest neighbor collaborative recommendation", Annals of Statistics 38 (2010): 1568--1592, arxiv:1010.0499
Kerstin Bunte, Michael Biehl and Barbara Hammer, "A General Framework for Dimensionality-Reducing Data Visualization Mapping", Neural Computation 24 (2012): 771--804
Burnham, Rise of the Computer State
Bertrand Clarke, "Desiderata for a Predictive Theory of Statistics", Bayesian Analysis 5 (2010): 1--36
Bertrand Clarke, Ernest Fokoue and Hao Helen Zhang, Principles and Theory for Data Mining and Machine Learning
Jesse Davis and Mark Goadrich, "The Relationship Between Precision-Recall and ROC Curves" [PDF preprint]
Pavel Dmitriev and Carl Lagoze, "Mining Generalized Graph Patterns based on User Examples", cs.DS/0609153
Usama Fayyad, Geroges G. Grinstein and Andreas Wierse (eds.), Information Visualization in Data Mining and Knowledge Discovery
Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data
Hillol Kargupta and Philip Chan (eds.), Advances in Distributed and Parallel Knolwedge Discovery
Robert L. Grossman and Richard G. Larson, "State Space Realization Theorems for Data Mining", arxiv:0901.2735
Hillol Kargupta, Anupam Joshi, Krishnamoorthy Sivakumar and Yelena Yesha, Data Mining: Next Generating Challenges and Future Directions
Nicholas M. Kiefer and C. Erik Larson, "Specification and Informational Issues in Credit Scoring", SSRN/956628
Ann B. Lee, Diana Luca and Kathryn Roeder, "A spectral graph approach to discovering genetic ancestry", Annals of Applied Statistics 4 (2010): 179--202
Colleen McCue, Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis [To be shot after a fair trial]
Michalski, Kubat, Bratko and Bratko (eds.), Machine Learning and Data Mining: Methods and Applications
Rada Mihaclea and Dragomir Radev, Graph-Based Natural Language Processing and Information Retrieval
Petra Kralj Novak, Nada Lavrac and Geoffrey I. Web,, "Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining", Journal of Machine Learning Research 10 (2009): 377--403
Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets
Naren Ramakrishnan and Chris Bailey-Kellogg, "Sampling Strategies for Mining in Data-Scarce Domains," cs.CE/0204047
Daniel J. Solove, "Data Mining and the Security-Liberty Debate", SSRN/990030
Joseph Turow, Niche Envy: Marketing Discrimination in the Digital Age
Christian H. Weiss, "Rule generation for categorical time series with Markov assumptions", Statistics and Computing 21 (2011): 1--16 [Variable-length Markov models]
Johannes Wollbold, "Attribute Exploration of Discrete Temporal Transitions", q-bio/0701009
Jun-Ming Xu, Aniruddha Bhargava, Robert Nowak, and Xiaojin Zhu, "Socioscope: Spatio-Temporal Signal Recovery from Social Media" [PDF]
Mohammed Javeed Zaki
- Scalable Data Mining for Rules [Ph.D. thesis, U. of Rochester, 1998; on-line through NCSTRL]
- "SPADE: An Efficient Algorithm for Mining Frequent Sequences," Machine Learning 42 (2001): 31--60