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Apriori Sets And Sequences
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Performance Data Collection
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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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