Top-k ϕ Correlation Computation

References

• Agrawal R., Imielinski T., Swami A. N. Mining association rules between sets of items in large databases. Proc. ACM SIGMOD Internat. Conf. Management Data (1993) (ACM Press, New York) 207–216Crossref, Google Scholar
• Agresti A.Categorical Data Analysis (2002) (Wiley Interscience, Hoboken, NJ) Wiley Series in Probability and StatisticsCrossref, Google Scholar
• Alexander C.Market Models: A Guide to Financial Data Analysis (2001) (John Wiley & Sons, New York) Google Scholar
• Bayardo R. J., Agrawal R., Gunopulos D. Constraint-based rule mining in large, dense databases. Data Mining Knowledge Discovery (2000) 4:217–240Crossref, Google Scholar
• Brin S., Motwani R., Silverstein C. Beyond market baskets: Generalizing association rules to correlations. Proc. ACM SIGMOD Internat. Conf. Management of Data (1997) (ACM Press, New York) 265–276Crossref, Google Scholar
• Bucila C., Gehrke J., Kifer D., White W. M. Dualminer: A dual-pruning algorithm for itemsets with constraints. Data Mining Knowledge Discovery (2003) 7:241–272Crossref, Google Scholar
• Burdick D., Calimlim M., Gehrke J. Mafia: A maximal frequent itemset algorithm for transactional databases. Proc. Internat. Conf. Data Engrg. (2001) (IEEE Computer Society, Washington, D.C.) 443–452Crossref, Google Scholar
• Cohen P., Cohen J., West S. G., Aiken L. S.Applied Multiple Regression/Correlation Analysis for the Behavioral Science (2002) 3rd ed.(Lawrence Erlbaum, Mahwah, NJ) Google Scholar
• Cohen E., Datar M., Fujiwara S., Gionis A., Indyk P., Motwani R., Ullman J. D., Yang C. Finding interesting associations without support pruning. Proc. Internat. Conf. Data Engrg. (2000) (IEEE Computer Society, Washington, D.C.) 489–499Crossref, Google Scholar
• DeGroot M., Schervish M.Probability and Statistics (2001) 3rd ed.(Addison-Wesley, Upper Saddle River, NJ) Google Scholar
• DuMouchel W., Pregibon D. Empirical bayes screening for multi-item associations. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (2001) (ACM Press, New York) 67–76Crossref, Google Scholar
• Grahne G., Lakshmanan L. V. S., Wang X. Efficient mining of constrained correlated sets. Proc. Internat. Conf. Data Engrg. (2000) (IEEE Computer Society, Washington D.C.) 512–521Crossref, Google Scholar
• Ilyas I. F., Markl V., Haas P. J., Brown P., Aboulnaga A. Cords: Automatic discovery of correlations and soft functional dependencies. Proc. ACM SIGMOD Internat. Conf. Management Data (2004) (ACM Press, New York) 647–658Crossref, Google Scholar
• Jermaine C. The computational complexity of high-dimensional correlation search. Proc. IEEE Internat. Conf. Data Mining (2001) (IEEE Computer Society, Washington, D.C.) 249–256Crossref, Google Scholar
• Jermaine C. Playing hide-and-seek with correlations. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (2003) (ACM Press, New York) 559–564Crossref, Google Scholar
• Kendall M., Gibbons J. D.Rank Correlation Methods (1990) 5th ed.(Oxford University Press, New York) Google Scholar
• Kuo W. P., Jenssen T.-K., Butte A. J., Ohno-Machado L., Kohane I. S. Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics (2002) 18:405–412Crossref, Google Scholar
• Lehmann E. L., D'Abrera H. J. M.Nonparametrics: Statistical Methods Based on Ranks (1998) (Prentice Hall, Upper Saddle River, NJ) Google Scholar
• Liu B., Hsu W., Ma Y. Mining association rules with multiple minimum supports. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (1999) (ACM Press, New York) 337–341Crossref, Google Scholar
• Ng R., Lakshmanan L. V. S., Han J., Pang A. Exploratory mining via constrained frequent set queries. Proc. ACM SIGMOD Internat. Conf. Management of Data (1999) (ACM Press, New York) 556–558Crossref, Google Scholar
• Rastogi R., Shim K. Mining optimized association rules with categorical and numeric attributes. IEEE Trans. Knowledge Data Engrg. (2002) 14:29–50Crossref, Google Scholar
• Reynolds H. T.The Analysis of Cross-Classifications (1977) (The Free Press, New York) Google Scholar
• Storch H. V., Zwiers F. W.Statistical Analysis in Climate Research (2002) Reprint ed.(Cambridge University Press, Cambridge, UK) Google Scholar
• Tan P.-N., Steinbach M., Kumar V.Introduction to Data Mining (2005) (Addison-Wesley, Upper Saddle River, NJ) Google Scholar
• Wang K., He Y., Cheung D. W.-L., Chin F. Y. L. Mining confident rules without support requirement. Proc. Internat. Conf. Inform. Knowledge Management (2001) (ACM Press, New York) 89–96Crossref, Google Scholar
• Xiong H., Shekhar S., Tan P., Kumar V. Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (2004) (ACM Press, New York) 334–343Crossref, Google Scholar
• Xiong H., He X., Ding C. H. Q., Zhang Y., Kumar V., Holbrook S. R., Altman R. B., Dunker A. K., Hunter L. Identification of functional modules in protein complexes via hyperclique pattern discovery. Proc. Pacific Sympos. Biocomputing 2005 (2005) (World Scientific Publishing Co. Pte. Ltd., Singapore) 221–232Google Scholar