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Thesis Report Latex
\newcommand{\etalchar}[1]{$^{#1}$} \begin{thebibliography}{DLM{\etalchar{+}}98} \bibitem[AAP00]{IBMDepthFirst} Ramesh~C. Agarwal, Charu~C. Aggarwal, and V.V.V. Prasad. \newblock Depth first generation of long patterns. \newblock In {\em Proc. of the Sixth ACM SIGKDD Conference on Knowledge Discover y and Data Mining}, pages 108--118, Boston, MA, Aug. 2000. ACM. \bibitem[AAP01]{agarwal01tree} Ramesh~C. Agarwal, Charu~C. Aggarwal, and V.~V.~V. Prasad. \newblock A tree projection algorithm for generation of frequent item sets. \newblock {\em Journal of Parallel and Distributed Computing}, 61(3):350--371, 2001. \newblock citeseer.ist.psu.edu/agarwal99tree.html. \bibitem[AFS93]{AFS93} R.~Agrawal, C.~Faloutsos, and A.~Swami. \newblock Efficient similarity search in sequence databases. \newblock In {\em Foundations of Data Organization and Algorithms (FODO) Conference}, Evanston, Illinois, Oct. 1993. \bibitem[AIS93]{AIS1993} R.~Agrawal, T.~Imielinski, and A.~Swami. \newblock Mining association rules between sets of items in large databases. \newblock In {\em Proc. of the ACM SIGMOD Conference on Management of Data}, pages 207--216, Washington, D.C., May 1993. ACM. \bibitem[All83]{A1983} J.F. Allen. \newblock Maintaining knowledge about temporal intervals. \newblock {\em Communications of the ACM}, 26(11), 1983. \bibitem[Alv03]{Alvarez:Chi2TR} Sergio~A. Alvarez. \newblock Chi--squared computation for association rules: Preliminary results. \newblock Technical Report BCCS-03-01, Computer Science Department, Boston College, July 2003. \bibitem[AR00]{AR2000} Juan~M. Ale and Gustavo~H. Rossi. \newblock An approach to discovering temporal association rules. \newblock In Janice Carroll, Ernesto Damiani, Hisham Haddad, and David Oppenheim, editors, {\em 2000 ACM Symposium on Applied Computing}, volume~1, pages 294--300, Como, Italy, 2000. ACM. \bibitem[AS94]{AS1994} R.~Agrawal and R.~Srikant. \newblock Fast algorithms for mining association rules. \newblock In {\em Proc. of the 20th VLDB Conference}, pages 487--499, Santiago, Chile, 1994. \bibitem[AS95]{agrawal.ea:sequential-patterns:95} R.~Agrawal and R.~Srikant. \newblock Mining sequential patterns. \newblock In {\em International Conference on Database Engineering}, pages 3--14. IEEE, 1995. \bibitem[AS96]{agrawal.ea:parallel-association:96} R.~Agrawal and J.~C. Shafer. \newblock Parallel mining of association rules. \newblock {\em {IEEE} Trans. On Knowledge And Data Engineering}, 8:962--969, 1996. \bibitem[BAG00]{BayardoEtAl:dmkd:2000} Roberto~J. Bayardo, Rakesh Agrawal, and Dimitrios Gunopulos. \newblock Constraint-based rule mining in large, dense databases. \newblock {\em Data Mining and Knowledge Discovery}, 4(2/3):217--240, July 2000. \bibitem[BC95]{BC1995} D.J. Berndt and J.~Clifford. \newblock Finding patterns in time series: a dynamic programming approach. \newblock In U.M. Fayyad, G.~Piatetsky-Shapiro, P.~Smyth, and R.~Uthurusamy, editors, {\em Advances in Knowledge Discovery and Data Mining}, pages 229--248. AAAI Press/ MIT Press, 1995. \bibitem[BFG{\etalchar{+}}03]{baird.ea:promoters-mqp:03} John Baird, Jay Farmer, Rebecca Gougian, Ken Monterio, and Paul Young. \newblock Motif elicitation and multipoint analysis of gene expression. \newblock Undergraduate Graduation Project ({MQP}). Worcester Polytechnic Institute, April 2003. \bibitem[BM98]{Blake+Merz:1998} C.L. Blake and C.J. Merz. \newblock {UCI} repository of machine learning databases, 1998. \newblock http://www.ics.uci.edu/$\sim$mlearn/MLRepository.html. \bibitem[BMS98]{SBM:dmkd:98} S.~Brin, R.~Motwani, and C.~Silverstein. \newblock Beyond market baskets: Generalizing association rules to dependence rules. \newblock {\em Data Mining and Knowledge Discovery}, 2:39--68, 1998. \bibitem[BSJ96]{bettini.ea:testing-complex:96} C.~Bettini, X.~{Sean Wang}, and S.~Jajodia. \newblock Testing complex temporal relationships involving multiple granularities and its application to data mining. \newblock In {ACM}, editor, {\em Proceedings of the Fifteenth {ACM} {SIGACT}-{SIGMOD}-{SIGART} Symposium on Principles of Database Systems, {PODS} 1996, Montr{\'e}al, Canada, June 3--5, 1996}, volume~15, pages 68--78, New York, NY 10036, USA, 1996. ACM Press. \bibitem[BWJL98]{BWJL1998} Claudio Bettini, X.~Sean Wang, Sushil Jajodia, and Jia-Ling Lin. \newblock Discovering frequent event patterns with multiple granularities in time sequences. \newblock {\em IEEE Transactions on Knowledge and Data Engineering}, 10(2):222--237, 1998. \bibitem[CDF{\etalchar{+}}00]{CohenEtAl:icde:2000} Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk, Rajeev Motwani, Jeffrey~D. Ullman, and Cheng Yang. \newblock Finding interesting associations without support pruning. \newblock In {\em {Proc. of Int'l. Conf. on Data Engineering (ICDE2000)}}, pages 489--499, 2000. \bibitem[Cor]{MS-perf} Microsoft Corporation. \newblock Msdn home page. \newblock http://msdn.microsoft.com/. \bibitem[CP99]{CP1999} X.~Chen and I.~Petrounias. \newblock Mining temporal features in association rules. \newblock In J.M. Zytkow and J.~Rauch, editors, {\em 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99)}, volume 1704 of {\em Lecture Notes in Artificial Intelligence}, pages 295--300, Prague, 1999. Springer. \bibitem[DLM{\etalchar{+}}98]{mannila:kdd98} Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, and Padhraic Smyth. \newblock Rule discovery from time series. \newblock In {\em Proc. of the 4th Int. Conf. on Knowledge Discovery and Data Mining}, pages 16--22. ACM, 1998. \bibitem[DP01]{DuMouchelPregibon:kdd01} W.~DuMouchel and D.~Pregibon. \newblock Empirical bayes screening for multi-item associations. \newblock In {\em Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining}, pages 67--76. ACM Press, 2001. \bibitem[Dun03]{dunham:textbook2003} Margaret~H. Dunham. \newblock {\em Data Mining, Introduction and Advanced Topics}. \newblock Pearson Education, Inc., 2003. \newblock From the Southern Methodist University, Chapter 7, Web Mining, pp. 195-219, Chapter 9, Temporal Mining, pp. 245-273. \bibitem[Fin]{Yahoo-Finance} Yahoo! Finance. \newblock Yahoo! finance. \newblock http://finance.yahoo.com/. \bibitem[FLYH99]{FLYH1999} Ling Feng, Hongjun Lu, J.~X. Yu, and Jiawei Han. \newblock Mining inter-transaction associations with templates. \newblock In {\em Proceedings of the 1999 ACM CIKM International Conference on Information and Knowledge Management, Kansas City, Missouri, USA, November 2-6, 1999}, pages 225--233. ACM, 1999. \bibitem[FM94]{FRM1994} M.~Ranganathan Faloutsos and Y.~Manolopoulos. \newblock Fast subsequence matching in time-series databases. \newblock In {\em Proc. ACM SIGMOD}, pages 419--429, Minneapolis, MN, May 1994. \bibitem[Fre04]{Freyberger:msthesis} Jonathan Freyberger. \newblock Using association rules to guide a search for best fitting transfer models of student learning. \newblock Master's thesis, Department of Computer Science, Worcester Polytechnic Institute, April 2004. \bibitem[FW00]{Weka} E.~Frank and I.~H. Witten. \newblock {\em Data Mining}. \newblock Morgan Kaufmann Publishers, 2000. \newblock From the University of Waikato, New Zealand. \bibitem[Hal00]{halwes.ea:-mqp:01} Tara Halwes. \newblock Transform-based similarity methods for sequence mining. \newblock Undergraduate Graduation Project ({MQP}). Worcester Polytechnic Institute, Aug. 2000. \newblock Winner, Provost's MQP Award for Computer Science. \bibitem[Hid98]{Hidber98} C.~Hidber. \newblock Online association rule mining. \newblock Technical Report CSD-98-1004, Dept. of Electrical Engineering and Computer Science, Univ. of California at Berkeley, May 1998. \bibitem[HK01]{Han:textbook2001} J.~Han and M.~Kamber. \newblock {\em Data Mining: Concepts and Techniques}. \newblock Morgan Kaufmann, 2001. \bibitem[HL03]{HL:financial-mqp:03} Sam Holmes and Cindy Leung. \newblock Exploring temporal associations in the stock market. \newblock Undergraduate Graduation Project ({MQP}). Worcester Polytechnic Institute, April 2003. \bibitem[HPY00]{HanPeiYin:2000} J.~Han, J.~Pei, and Y.~Yin. \newblock Mining frequent patterns without candidate generation. \newblock In {\em Proc. Int'l. Conf. of Management of Data (SIGMOD2000)}, pages 1--12, May 2000. \bibitem[Lai93]{L1993} P.~Laird. \newblock Identifying and using patterns in sequential data. \newblock In Klaus~P. Jantke, Shigenobu Kobayashi, Etsuji Tomita, and Takashi Yokomori, editors, {\em Proceedings of The 4th International Workshop on Algorithmic Learning Theory, ALT '93, Tokyo, Japan, November 8-10, 1993}, Lecture Notes in Computer Science, pages 1--18, Tokyo, Japan, 1993. Springer. \bibitem[Lax04]{Paramesh:msthesis} Parameshvyas Laxminarayan. \newblock Exploratory analysis of human sleep data. \newblock Master's thesis, Department of Computer Science, Worcester Polytechnic Institute, January 2004. \bibitem[LFH00]{han:tois2000} Hongjun Lu, Ling Feng, and Jiawei Han. \newblock Beyond intratransaction association analysis: mining multidimensional intertransaction association rules. \newblock {\em ACM Transactions on Information Systems (TOIS)}, 18(4):423--454, 2000. \bibitem[Lin98]{Lin:nyu:98} D-I Lin. \newblock {\em Fast Algorithms for Discovering the Maximum Frequent Set}. \newblock PhD thesis, Dept. of Computer Science. New York University, 1998. \bibitem[LR91]{LR91:financialtemplates} Jeffrey Little and Lucien Rhodes. \newblock {\em Understanding Wall Street}. \newblock Liberty Hall Press and McGraw-Hill Trade, 3rd edition, 1991. \bibitem[LWJ00]{LWJ00} Yingjiu Li, X.~Sean Wang, and Sushil Jajodia. \newblock Discovering temporal patterns in multiple granularities. \newblock In J.~F. Roddick and K.~Hornsby, editors, {\em International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000}, volume 2007 of {\em Lecture Notes in Artificial Intelligence}, Lyon, France, 2000. Springer. \bibitem[MT96]{MT96} H.~Mannila and H.~Toivonen. \newblock Discovering generalized episodes using minimal occurrences. \newblock In {\em Proc. of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96)}, pages 146--151, Portland, Oregon, August 1996. AAAI Press. \bibitem[MTV95]{MTV1995} H.~Mannila, H.~Toivonen, and A.~I. Verkamo. \newblock {Discovering Frequent Episodes in Sequences}. \newblock In U.~M. Fayyad and R.~Uthurusamy, editors, {\em Proceedings of the First International Conference on Knowledge Discovery and Data Mining ({KDD}-95)}, Montreal, Canada, 1995. AAAI Press. \bibitem[OSGC95]{oates94detecting} Tim Oates, Matthew~D. Schmill, Dawn~E. Gregory, and Paul~R. Cohen. \newblock Detecting complex dependencies in categorical data. \newblock Technical Report UM-CS-1994-081, University of Massachusetts, Amherst, MA, , 1995. \newblock citeseer.nj.nec.com/oates94detecting.html. \bibitem[RMS98]{RMS1998} Sridhar Ramaswamy, Sameer Mahajan, and Abraham Silberschatz. \newblock On the discovery of interesting patterns in association rules. \newblock In Ashish Gupta, Oded Shmueli, and Jennifer Widom, editors, {\em VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA}, pages 368--379. Morgan Kaufmann, 1998. \bibitem[RR99]{RainsfordRoddick99:PKDD} C.P. Rainsford and John~F. Roddick. \newblock Adding temporal semantics to association rules. \newblock In J.M. Zytkow and J.~Rauch, editors, {\em 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99)}, volume 1704 of {\em Lecture Notes in Artificial Intelligence}, pages 504--509, Prague, 1999. Springer. \bibitem[RS01]{RoddickSpiliopoulou2001:Survey} John~F. Roddick and M.~Spiliopoulou. \newblock A survey of temporal knowledge discovery paradigms and methods. \newblock {\em IEEE Transactions on Knowledge and Data Engineering}, 13, 2001. \bibitem[RS02]{RoddickSpiliopoulou:tkde02} J.F. Roddick and M.~Spiliopoulou. \newblock A survey of temporal knowledge discovery paradigms and methods. \newblock {\em IEEE Transactions on Knowledge and Data Engineering}, 14(4):750--767, 2002. \bibitem[SA96]{SA1996} R.~Srikant and R~Agrawal. \newblock Mining sequential patterns: Generalizations and performance improvements. \newblock In {\em Proc. of the Fifth Int'l Conference on Extending Database Technology (EDBT)}, Avignon, France, March 1996. \bibitem[Sho01]{Shoemaker:msthesis:2001} C.A. Shoemaker. \newblock Mining association rules from set-valued data. \newblock Master's thesis, Department of Computer Science, Worcester Polytechnic Institute, May 2001. \bibitem[SR03]{SR03:setassocrules} C.~Shoemaker and C.~Ruiz. \newblock Association rule mining algorithms for set-valued data. \newblock In J.~Liu, Y.~Cheung, and H.~Yin, editors, {\em Proc. of the Fourth International Conference on Intelligent Data Engineering and Automated Learning}, pages 669--676. Lecture Notes in Computer Science. Vol. 2690. Springer-Verlag, June 2003. \bibitem[SS02]{zack:wekarminer-mqp:02} Zachary Stoecker-Sylvia. \newblock Merging the association rule mining modules of the weka and arminer data mining systems. \newblock Undergraduate Graduation Project ({MQP}). Worcester Polytechnic Institute, April 2002. \bibitem[TLHF03]{TLHF2003} Anthony~K.H. Tung, Hongjun Lu, Jiawei Han, and Ling Feng. \newblock Efficient mining of intertransaction association rules. \newblock {\em IEEE Transactions On Knowledge And Data Engineering}, 15(1):43--56, January/February 2003. \bibitem[WBY03]{WuBarbaraYe:kdd03} Xintao Wu, Daniel Barbar{\'a}, and Yong Ye. \newblock Screening and interpreting multi-item associations based on log-linear modeling. \newblock In {\em Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining}, pages 276--285. ACM Press, 2003. \bibitem[Web00]{Webb2000} Geoffrey~I. Webb. \newblock Efficient search for association rules. \newblock In {\em Proc. of the Sixth ACM SIGKDD Conference on Knowledge Discover y and Data Mining}, pages 99--107, Boston, MA, Aug. 2000. ACM. \bibitem[XD99]{XiaoDunham:dwkd:99} Yongqiao Xiao and Margaret~H. Dunham. \newblock Considering main memory in mining association rules. \newblock In {\em Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery}, pages 209--218, November 1999. \bibitem[Zak00]{zaki:cikm2000} M.~Zaki. \newblock Sequence mining in categorical domains: Incorporating constraints. \newblock In {\em Proc. Int. Conference on Information and Knowledge Management (CIKM)}, pages 422--429. ACM, 2000. \bibitem[ZKM01]{ZhengKohaviMason:kdd01} Z.~Zheng, R.~Kohavi, and L.~Mason. \newblock Real world performance of association rule algorithms. \newblock In {\em Proceedings of the Seventh ACM--SIGKDD International Conference on Knowledge Discovery and Data Mining}, 2001. \bibitem[ZOPL96]{zaki.ea:parallel-association:96a} M.~J. Zaki, M.~Ogihara, S.~Parthasarathy, and W.~Li. \newblock Parallel data mining for association rules on shared-memory multi-processors. \newblock In {\em CD-ROM Proceedings of Supercomputing'96}, Pittsburgh, PA, November 1996. IEEE. \bibitem[ZPOL97]{zaki.ea:full-new-algorithms:97} M.~J. Zaki, S.~Parthasarathy, M.~Ogihara, and W.~Li. \newblock New algorithms for fast discovery of association rules. \newblock In David Heckerman, Heikki Mannila, Daryl Pregibon, and Ramasamy Uthurusamy, editors, {\em Proceedings of the Third International Conference on Knowledge Discovery and Data Mining ({KDD}-97)}, page 283. AAAI Press, 1997. \end{thebibliography} |
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