Validation Sequence Optimization: A Theoretical Approach
Published Online:1 May 2007https://doi.org/10.1287/ijoc.1050.0153
References
- Expert-driven validation of set-based data mining results. (2002) . Ph.D. thesis, Computer Science Department, New York University, New York, http://www.cs.nyu.edu/web/Research/theses.htmlGoogle Scholar
- User profiling in personalization applications through rule discovery and validation. Fifth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (1999) 377–381Crossref, Google Scholar
- Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery (2001) 5:33–58Crossref, Google Scholar
- A structured methodology for developing production systems. Decision Support Systems (1992) 8:483–499Crossref, Google Scholar
- , Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining (1996) (MIT Press, Cambridge, MA) . Chap. 12Google Scholar
- Adaptive ordering of pipelined stream filters. ACM SIGMOD Internat. Conf. Management Data (2004) 407–418Crossref, Google Scholar
- Concurrency Control and Recovery in Database Systems (1987) (Addison Wesley, Reading, MA) Google Scholar
- , Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. The process of knowledge discovery in databases: A human-centered approach. Advances in Knowledge Discovery and Data Mining (1996) (MIT Press, Cambridge, MA) . Chap. 2Google Scholar
- Introduction to Algorithms (2001) 2nd ed.(MIT Press, Cambridge, MA) Google Scholar
- , Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. From data mining to knowledge discovery: An overview. Advances in Knowledge Discovery and Data Mining (1996) (MIT Press, Cambridge, MA) . Chap. 1Google Scholar
- Computers and Intractability: A Guide to the Theory of NP-Completeness (1979) (W. H. Freeman and Company, New York) Google Scholar
- A model-based approach to investigate performance improvement in rule-based expert systems. Decision Sci. (1993) 24:42–59Crossref, Google Scholar
- MSQL: A query language for database mining. Data Mining and Knowledge Discovery (1999) 3:373–408Crossref, Google Scholar
- Finding interesting rules from large sets of discovered association rules. Third Internat. Conf. Inform. and Knowledge Management (1994) 401–407Crossref, Google Scholar
- Knowledge-based learning in exploratory science: Learning rules to predict rodent carcinogenicity. Machine Learn. (1998) 30:217–240Crossref, Google Scholar
- Post-analysis of learned rules. AAAI Conf. (1996) 828–834Google Scholar
- Pruning and summarizing the discovered associations. Fifth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (1999) 125–134Crossref, Google Scholar
- Evaluating knowledge discovery and data mining. Tutorial Fourth Internat. Conf. Knowledge Discovery and Data Mining (1998) Google Scholar
- Interestingness via what is not interesting. Fifth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (1999) 332–336Crossref, Google Scholar
- User-assisted knowledge discovery: How much should the user be involved? SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (1996) (Montreal, Canada)Google Scholar
- Mining association rules with item constraints. Third Internat. Conf. Knowledge Discovery and Data Mining (1997) 67–73Google Scholar
- Handling very large numbers of association rules in the analysis of microarray data. Eighth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (2002) 396–404Crossref, Google Scholar
- Querying multiple sets of discovered rules. Eighth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (2002) 52–60Crossref, Google Scholar

