Data Mining

24 Dec 2008 10:31

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.

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]
David Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining [The textbook I assigned; also a book I learned a lot from]
Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age [Blurb, my review. Precis as a 43 pp. PDF working paper]
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]
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
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]

David M. Blei, A. Y. Ng and Michael I. Jordan, "Latent Dirichlet allocation", Journal of Machine Learning REsearch 3 (2003): 993--1022
David M. Blei and John D. Laffery, "Correlated Topic Models", NIPS 2005
Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers, "Text Modeling using Unsupervised Topic Models and Concept Hierarchies", arxiv:0808.0973
Thomas Hofmann, "Unsupervised Learning by Probabilistic Latent Semantic Analysis", Machine Learning 42 (2001): 177--196
T. K. Landauer and S. T. Dumais, "A Solution to Plato's Problem: The Latent Sematic Analysis Theory of the Acquisition, Induction and Representation of Knowledge", Psychological Review 104 (1997):211--240

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.]

Ian Ayres, Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart [Despite the painful title, Ayres has done some very interesting applied work in social statistics]
David L. Banks and Yasmin H. Said, "Data Mining in Electronic Commerce", math.ST/0609204 = Statistical Science 21 (2006): 234--246
Burnham, Rise of the Computer State
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
Ronen Feldman and James Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data [Blurb
Hillol Kargupta and Philip Chan (eds.), Advances in Distributed and Parallel Knolwedge Discovery [Blurb]
Hillol Kargupta, Anupam Joshi, Krishnamoorthy Sivakumar and Yelena Yesha, Data Mining: Next Generating Challenges and Future Directions [Blurb]
Nicholas M. Kiefer and C. Erik Larson, "Specification and Informational Issues in Credit Scoring" [SSRN]
Martin Klein and Michael L. Nelson, "Approximating Document Frequency with Term Count Values", arxiv:0807.3755 [Approximating inverse document frequency for the web (or other unsurveyable corpora) by term frequency]
Jacob Kogan, Introduction to Clustering Large and High-Dimensional Data Blurb]
Colleen McCue, Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis
Michalski, Kubat, Bratko and Bratko (eds.), Machine Learning and Data Mining: Methods and Applications
Naren Ramakrishnan and Chris Bailey-Kellogg, "Sampling Strategies for Mining in Data-Scarce Domains," cs.CE/0204047
Juan J. Samper, Pedro A. Castillo, Lourdes Araujo, and J. J. Merelo, "NectaRSS, an RSS feed ranking system that implicitly learns user preferences", cs.IR/0610019
Jeffrey Solka, "Text Data Mining: Theory and Methods", Statistical Surveys 2 (2008): 94--112 = arxiv:0807.2569
Daniel J. Solove, "Data Mining and the Security-Liberty Debate" [SSRN/990030]
Andreas L. Symeonidis and Pericles A. Mitkas, Agent Intelligence through Data Mining [Blurb]
Joseph Turow, Niche Envy: Marketing Discrimination in the Digital Age [Blurb]
Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
Johannes Wollbold, "Attribute Exploration of Discrete Temporal Transitions", q-bio/0701009
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