Resumos

• Allison Chang, Cynthia Rudin, and Dimitris Bertsimas. Ordered Rules for Classification: A Discrete Optimization Approach to Associative Classification, MIT Working Paper, NIPS 2012. [bibtex][resumo]
• Jerome H Friedman, Bogdan E Popescu, Predictive Learning Via Rule Ensembles, The Annals of Applied Statistics, volume 2 issue 3 (1 September 2008) [bibtex]
• Adam Kirsch , Michael Mitzenmacher , Andrea Pietracaprina , Geppino Pucci , Eli Upfal , Fabio Vandin, An efficient rigorous approach for identifying statistically significant frequent itemsets, Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 29-July 01, 2009, Providence, Rhode Island, USA [bibtex]
• Benjamin Letham, Cynthia Rudin, Tyler McCormick and David Madigan. Building Interpretable Classifiers with Rules using Bayesian Analysis [bibtex][resumo]
• D. Reshef*, Y. Reshef*, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, E. Lander, M. Mitzenmacher**, P. Sabeti** (*,** contributed equally)
  • Detecting Novel Associations in Large Datasets, Science 2011. [bibtex]
• Johannes Fuernkranz, Peter Flach, An Analysis of Stopping and Filtering Criteria for Rule Learning. Proceedings of the 15th European Conference on Machine Learning. ISBN 3-540-23105-6, pp. 123–133. September 2004 [bibtex][resumo]
• Usama M. Fayyad, Keki B. Irani: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. IJCAI 1993: 1022-1029 [bibtex][resumo]
• Pang-Ning Tan , Vipin Kumar , Jaideep Srivastava, Selecting the right interestingness measure for association patterns, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775053][bibtex][resumo]
• Mohammed J. Zaki , Ching-Jui Hsiao, Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure, IEEE Transactions on Knowledge and Data Engineering, v.17 n.4, p.462-478, April 2005 [doi>10.1109/TKDE.2005.60][bibtex]
• Gyorgy J. Simon , Vipin Kumar , Peter W. Li, A simple statistical model and association rule filtering for classification, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, August 21-24, 2011, San Diego, California, USA [doi>10.1145/2020408.2020550][bibtex][resumo]
• Stephan Börzsönyi , Donald Kossmann , Konrad Stocker, The Skyline Operator, Proceedings of the 17th International Conference on Data Engineering, p.421-430, April 02-06, 2001[bibtex]
• Parke Godfrey , Ryan Shipley , Jarek Gryz, Algorithms and analyses for maximal vector computation, The VLDB Journal — The International Journal on Very Large Data Bases, v.16 n.1, p.5-28, January 2007 [bibtex]
• A. Veloso, M. Zaki, W. Meira Jr., and M. Gonalves, The metric dilemma: Competenceconscious associative classification, Proceedings of the Ninth SIAM International Conference on Data Mining, April-May, 2009,pp. 918-929 [bibtex]
• Y.W. Choong, L. Di-Jorio, A. Laurent, D. Laurent, and M. Teisseire, CBGP: Classification Based on Gradual Patterns. ;In Proceedings of SOCPAR. 2009, 7-12. [bibtex]
• Jinyan Li , Haiquan Li , Limsoon Wong , Jian Pei , Guozhu Dong, Minimum description length principle: generators are preferable to closed patterns, Proceedings of the 21st national conference on Artificial intelligence, p.409-414, July 16-20, 2006, Boston, Massachusetts[bibtex]
• Adriano Veloso , Wagner Meira, Jr. , Marcos Gonçalves , Humberto M. Almeida , Mohammed Zaki, Calibrated lazy associative classification, Information Sciences: an International Journal, v.181 n.13, p.2656-2670, July, 2011 [bibtex][resumo]
• Mohammed J. Zaki , Karam Gouda, Fast vertical mining using diffsets, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.[bibtex][resumo]
• Mohammed J Zaki , Srinivasan Parthasarathy , Mitsunori Ogihara , Wei Li, New Algorithms for Fast Discovery of Association Rules, University of Rochester, Rochester, NY, 1997 [bibtex]
• Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994. [bibtex]
• Volker Nannem,A Short Introduction to Model Selection, Kolmogorov Complexity and Minimum Description Length (MDL), 2003, Thesis, Rijksuniversiteit Groningen. [bibtex][resumo]
• G. Schmidberger and E. Frank. Unsupervised discretization using tree-based density estima-tion. In PKDD ’05, pages 240–251. [bibtex]
• P.D. Grünwald, A Tutorial Introduction to the Minimum Description Principle, MIT Press, April 2005. [bibtex]
• Rodrigo Silva, Marcos A. Gonçalves, Adriano Veloso, Rule-based active sampling for learning to rank,ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III [bibtex]
• Ishida, C.Y. and Pozo, A.T.R., Optimization of the AUC Criterion for Rule Subset Selection , Intelligent Systems Design and Applications, 2007. ISDA [bibtex]
• Batista, G. E. A. P. A., Milar ́ , C. R., Prati, R. C., and Monard, M. C. (2006). A comparison of methods for rule subset selection applied to associative classification. Inteligencia Artificial Iberoamerican Journal of AI, (32):29–35 [bibtex][resumo]
• Viktor Jovanoski , Nada Lavrac, Classification Rule Learning with APRIORI-C, Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving, p.44-51, December 17-20, 200 [bibtex][resumo]
• Provost, F., Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distribution. In Proceedings of the third international conference on knowledge discovery and data mining (KDD-97) (pp. 43-48). [bibtex][resumo]
• Tom Fawcett , Alexandru Niculescu-Mizil, PAV and the ROC convex hull, Machine Learning, v.68 n.1, p.97-106, July 2007 [bibtex][resumo]
• Ronaldo C. Prati , Peter A. Flach, ROCCER: an algorithm for rule learning based on ROC analysis, Proceedings of the 19th international joint conference on Artificial intelligence, p.823-828, July 30-August 05, 2005, Edinburgh, Scotland [bibtex]
• Tom Fawcett, 2008, PRIE: A system for generating rulelists to maximize ROC performance, Data Mining and Knowledge Discovery, Volume 17, Number 2 / October, 2008. Pages 207 - 224. DOI: 10.1007/s10618-008-0089-y [bibtex]
• Fawcett, T.: 2006, ‘An Introduction to ROC Analysis’. Pattern Recognition Letters 27(8), 882–891 [bibitex]
• Tom Fawcett, ROC Graphs: Notes and Practical Considerations for Researchers, In HP Labs Tech Report, No. HPL-2003-4. [bibtex]
• Farial Shahnaz , Michael W. Berry , V. Paul Pauca , Robert J. Plemmons, Document clustering using nonnegative matrix factorization, Information Processing and Management: an International Journal, v.42 n.2, p.373-386, March 2006 [doi>10.1016/j.ipm.2004.11.005][bibtex]
• C. A. S. Bigonha, T. N. C. Cardoso, M. M. Moro, V. A. F. Almeida, M. A. Gonçalves. Detecting Evangelists and Detractors on Twitter. Best Paper Award. In: Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2010, Belo Horizonte, Brazil. [bibtex]
• SILVA, I. S.; GOMIDE. J.; VELOSO, A.; FERREIRA, R. and MEIRA Jr., W. Effective Sentiment Stream Analysis with Self-Augmenting Training and Demand-Driven Projection. SIGIR, 2011.[bibtex][resumo]

• Johan Bollen, Huina Mao, and Xiao-Jun Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2(1), March 2011, Pages 1-8, doi:10.1016/j.jocs.2010.12.007, arxiv: abs/1010.3003. (featured on CNBC, CNN International and Bloomberg News!)[bibtex][resumo]
• SILVA, I. S.; GOMIDE. J.; BARBOSA, G.; VELOSO, A.; SANTOS, W.; FERREIRA, R. and MEIRA Jr., W. Observatório da Dengue: Surveillance based on Twitter Sentiment Stream Analysis. SBBD, 2011.
• Atsalakis, G. S.& Valavanis, K.P., Surveying stock market forecasting techniques – Part II: Soft computing
• methods, Expert Systems with Applications (2008), doi:10.1016/j.eswa.2008.07.006 [bibtex][resumo]
• Conegundes, L. ; Hora, D. ; Palotti, J. ; Meira Jr, W. ; Pappa, Gisele L. From an Artificial Neural Network to a Stock Market Day-Trading System: A Case Study on the BMF BOVESPA. In: 2009 International Joint Conference on Neural Networks, 2009, Atlanta [bibtex][resumo]
• Gomide, P.; Milidiú, R. L. (2010) Assessing Stock Market Time Series Predictors Quality Through a Pairs Trading System
• XI Brazilian Symposium on Neural Network (SBRN), São Bernardo do Campo, SP, Brazil [bibtex][resumo]
• Atiya, A., Noha Talaat & Samir Shaheen, (1997). An efficient stock market forecasting model using neural networks. In Proceedings of the IEEE International Conference on Neural Networks. [bibtex][resumo]
• Zhang, X. Fuehres, H. Gloor, P. Predicting Stock Market Indicators Through Twitter: “I hope it is not as bad as I fear”, Procedia - Social and Behavioral Sciences, Volume 26, 2011, Collaborative Innovations Networks Conference, Savannah GA, Oct 7-9, 2010[bibtex]

Informações Genéricas

Wall Street Birds Pro Software: software para tomada de decisões na bolsa que utiliza dados do twitter. Cita como exemplo o artigo Twitter mood predicts the stock market

Granger causality test: is a statistical hypothesis test for determining whether one time series is useful in forecasting another.

TweetTrader.net strives to be the most innovative financial information market for stock microblogs. Twitter has become a vibrant forum to exchange financial information and trading advice.

http://quant.stackexchange.com/ forum

StockTwits operates as an online community that provides information about investments and stocks. It provides users to give an opinion about a specific stock symbol through a tweet

Reportagens

• AllTwitter Economists Use Twitter To Beat The Stock MarketBy Shea Bennett on April 6, 2011 6:08 AM
  • WIRED Twitter Can Predict the Stock Market By Lisa Grossman Email Author October 19, 2010
• ScienceBlockg Twitter analysis provides stock predictions
• CNN Twitter hedge fund beats market August 17th, 2011 02:00 PM ET Post by: Global Post
• The Atlantic The World's First Twitter-Based Hedge Fund Is Finally Open for Business May 18 2011, 2:22 PM ET Post by: Derek Thompson
• BBC NEWS Twitter predicts future of stocks 6 April 2011
  • Business WeekStockTwits May Change How You Trade February 11, 2009, 12:01AM EST By Max Zeledon