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Apriori Sets And Sequences
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Performance Data Collection
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Thesis Report Latex
% INPROCEEDINGS -----------------------------------------------------------%
@article{RoddickSpiliopoulou2001:Survey,
Author = {Roddick, John F. and Spiliopoulou, M.},
Title = {A Survey of Temporal Knowledge Discovery Paradigms and Methods},
Journal = {IEEE Transactions on Knowledge and Data Engineering},
Volume = {13},
Abstract = {With the increase in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and an increasing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behaviour associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining.},
Keywords = {DM,STDMBib1, STDM2MISC
BestRef KDMLab},
Year = {2001}
}
@inproceedings{RainsfordRoddick99:PKDD,
Author = {Rainsford, C.P. and Roddick, John F.},
Title = {Adding Temporal Semantics to Association Rules},
BookTitle = {3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99)},
Editor = {Zytkow, J.M. and Rauch, J.},
Series= {Lecture Notes in Artificial Intelligence},
Address= {Prague},
Publisher = {Springer},
Volume = {1704},
Pages = {504-509},
Abstract = {The development of systems for knowledge discovery in databases, including the use of association rules, has become a major research issue in recent years. Although initially motivated by the desire to analyse large retail transaction databases, the general utility of association rules makes them applicable to a wide range of different learning tasks. However association rules do not accommodate the temporal relationships that may be intrinsically important in some application domains. In this paper we present an extension to association rules to accommodate temporal semantics. By finding associated attributes first and then looking for temporal relationships between them, these temporal association rules incorporate potentially valuable temporal semantics. Our approach to temporal reasoning accommodates both event-based and interval-based models of time simultaneously. In addition, the use of a generalised taxonomy of relationships supports the generalisation of temporal relationships and their specification at different levels of abstraction. This approach also facilitates the possibility of reasoning with incomplete or missing information. Preliminary experimental results have indicated that the cost of adding temporal knowledge is of the same order as the cost of learning the initial association rules.},
Keywords = {DM, STDMBib1.1 STDM2Ass KDMLab},
Year = {1999}
}
@article{A1983,
address = {},
author = {Allen, J.F.},
editor = {},
journal = {Communications of the ACM},
month = {},
organization = {ACM},
pages = {},
publisher = {},
title = {Maintaining knowledge about temporal intervals},
year = {1983},
volume = {26},
number = {11}
}
@inproceedings{AFS93,
address = {Evanston, Illinois},
author = {R. Agrawal and C. Faloutsos and A. Swami},
editor = {},
booktitle = {Foundations of Data Organization and Algorithms
(FODO) Conference},
month = {Oct.},
organization = {},
pages = {},
publisher = {},
title = {Efficient Similarity Search in Sequence Databases},
year = {1993},
}
@inproceedings{AIS1993,
author = {R. Agrawal and T. Imielinski and A. Swami},
title = {Mining Association Rules between Sets of Items in Large Databases},
booktitle = {Proc. of the ACM SIGMOD Conference on Management of Data},
year = {1993},
month = {May},
editor = {},
organization = {ACM},
publisher = {},
address = {Washington, D.C.},
pages = {207-216},
note = {},
}
@inproceedings{AS1994,
author = {R. Agrawal and R. Srikant},
title = {Fast Algorithms for Mining Association Rules},
booktitle = {Proc. of the 20th VLDB Conference},
year = {1994},
month = {},
editor = {},
organization = {},
publisher = {},
address = {Santiago, Chile},
pages = {487-499},
note = {},
}
@inproceedings{FRM1994,
address = {Minneapolis, MN},
author = {Faloutsos, M. Ranganathan and Y. Manolopoulos},
editor = {},
booktitle = {Proc. ACM SIGMOD},
month = {May},
organization = {},
pages = {419--429},
publisher = {},
title = {Fast Subsequence Matching in Time-Series Databases},
year = {1994},
}
@inproceedings{SA1996,
address = {Avignon, France},
author = {Srikant, R. and Agrawal, R},
editor = {},
booktitle = {Proc. of the Fifth Int'l Conference on Extending
Database Technology (EDBT)},
month = {March},
organization = {},
pages = {},
publisher = {},
title = {Mining Sequential Patterns: Generalizations and
Performance Improvements},
year = {1996},
}
@inproceedings{L1993,
address = {Tokyo, Japan},
author = {Laird, P.},
editor = {Klaus P. Jantke and
Shigenobu Kobayashi and
Etsuji Tomita and
Takashi Yokomori},
booktitle = {Algorithmic Learning Theory, 4th International
Workshop, ALT '93, Tokyo, Japan, November 8-10,
1993, Proceedings},
series = {Lecture Notes in Computer Science},
month = {},
organization = {},
pages = {1-18},
publisher = {Springer},
title = {Identifying and Using Patterns in Sequential Data},
year = {1993},
}
@inproceedings{FLYH1999,
author = {Ling Feng and
Hongjun Lu and
J. X. Yu and
Jiawei Han},
title = {Mining Inter-Transaction Associations with Templates},
booktitle = {Proceedings of the 1999 ACM CIKM International Conference on
Information and Knowledge Management, Kansas City, Missouri,
USA, November 2-6, 1999},
publisher = {ACM},
year = {1999},
isbn = {1-58113-146-1},
pages = {225-233},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
@article{TLHF2003,
author = {Anthony K.H. Tung and
Hongjun Lu and
Jiawei Han and
Ling Feng},
title = {Efficient Mining of InterTransaction Association Rules},
journal = {IEEE Transactions On Knowledge And Data Engineering},
publisher = {IEEE},
year = {2003},
month = {January/February},
isbn = {},
pages = {43-56},
volume = {15},
number = {1},
bibsource = {}
}
@inproceedings{RMS1998,
author = {Sridhar Ramaswamy and
Sameer Mahajan and
Abraham Silberschatz},
editor = {Ashish Gupta and
Oded Shmueli and
Jennifer Widom},
title = {On the Discovery of Interesting Patterns in Association Rules},
booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very
Large Data Bases, August 24-27, 1998, New York City, New York,
USA},
publisher = {Morgan Kaufmann},
year = {1998},
isbn = {1-55860-566-5},
pages = {368-379},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
@inproceedings { MTV1995,
author = "H. Mannila and H. Toivonen and A. I. Verkamo",
title = "{Discovering Frequent Episodes in Sequences}",
booktitle = "Proceedings of the First International Conference on Knowledge Discovery and Data Mining ({KDD}-95)",
publisher = "AAAI Press",
address = "Montreal, Canada",
editor = "U. M. Fayyad and R. Uthurusamy",
year = "1995"
}
@inproceedings {DLMRS1998,
author = "Gautam Das and
King-Ip Lin and
Heikki Mannila and
Gopal Renganathan and
Padhraic Smyth",
title = "Rule Discovery from Time Series",
booktitle = "Knowledge Discovery and Data Mining",
pages = "16-22",
year = "1998"
}
@inproceedings { AR2000,
Author = {Ale, Juan M. and Rossi, Gustavo H.},
Title = {An Approach To Discovering Temporal Association Rules},
BookTitle = {2000 ACM Symposium on Applied Computing},
Editor = {Carroll, Janice and
Damiani, Ernesto and
Haddad, Hisham and
Oppenheim, David},
Address = {Como, Italy},
Publisher = {ACM},
Volume = {1},
Pages = {294-300},
Keywords = {STDM2ASS},
Year = {2000}
}
@inproceedings{CP1999,
Author = {Chen, X. and Petrounias, I.},
Title = {Mining Temporal Features in Association Rules},
BookTitle = {3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99)},
Editor = {Zytkow, J.M. and Rauch, J.},
Series= {Lecture Notes in Artificial Intelligence},
Address= {Prague},
Publisher = {Springer},
Volume = {1704},
Pages = {295-300},
Keywords = {STDMBib1.1 STDM2Ass},
Year = {1999} }
@inproceedings{LWJ00,
Author = {Yingjiu Li and
X. Sean Wang and
Sushil Jajodia},
Title = {Discovering Temporal Patterns in Multiple Granularities},
BookTitle = {International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000},
Editor = {Roddick, J. F. and Hornsby, K.},
Series= {Lecture Notes in Artificial Intelligence},
Address= {Lyon, France},
Publisher = {Springer},
Volume = {2007},
Keywords = {STDM2PATT},
Year = {2000} }
% / INPROCEEDINGS -----------------------------------------------------------%
@incollection{BC1995,
Author = {Berndt, D.J. and Clifford, J.},
Title = {Finding patterns in time series: a dynamic programming approach},
BookTitle = {Advances in Knowledge Discovery and Data Mining},
Editor = {Fayyad, U.M. and Piatetsky-Shapiro, G. and Smyth, P. and Uthurusamy, R.},
Publisher = {AAAI Press/ MIT Press},
Pages = {229-248},
Keywords = {STDMBib1.4, data mining, temporal, time series STDM2Series},
Year = {1995} }
@article{BWJL1998,
Author = {Claudio Bettini and
X. Sean Wang and
Sushil Jajodia and
Jia-Ling Lin},
Title = {Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences},
Journal = {IEEE Transactions on Knowledge and Data Engineering},
Volume = {10},
Number = {2},
Pages = {222-237},
Keywords = {},
Year = {1998} }
%----------------------------------------------------------------------------%
% MISC
@book{Weka,
author = {E. Frank and I. H. Witten},
title = {Data Mining},
year = {2000},
publisher = {Morgan Kaufmann Publishers},
note = {From the University of Waikato, New Zealand}
}
@book{D2003,
author = {Margaret H. Dunham},
title = {Data Mining, Introduction and Advanced Topics},
year = {2003},
publisher = {Pearson Education, Inc.},
note = {From the Southern Methodist University, Chapter 7, Web Mining, pp. 195-219, Chapter 9, Temporal Mining, pp. 245-273},
}
@techreport{oates94detecting,
author = "Tim Oates and
Matthew D. Schmill and
Dawn E. Gregory and
Paul R. Cohen",
title = "Detecting Complex Dependencies in Categorical Data",
number = "UM-CS-1994-081",
month = ",",
year = "1995",
url = "citeseer.nj.nec.com/oates94detecting.html",
institution = "University of Massachusetts, Amherst, MA"
}
@TECHREPORT{Alvarez:Chi2TR,
AUTHOR = {Sergio A. Alvarez},
TITLE = {Chi--Squared Computation for Association Rules: Preliminary Results},
INSTITUTION = {Computer Science Department, Boston College},
ABSTRACT = {
Chi-squared analysis is useful in determining the
statistical significance level of association rules.
We show that the chi squared statistic of a rule may
be computed directly from the values of confidence,
support, and lift (interest) of the rule in question.
Our results facilitate pruning of rule sets obtained
using standard association rule mining techniques,
allow identification of statistically significant rules
that may have been overlooked by the mining algorithm,
and provide an analytical description of the relationship
between confidence and support in terms of chi--squared and lift.
},
BOSTONCOLLEGECOMPUTERSCIENCE = {yes},
YEAR = 2003,
MONTH = {July},
NUMBER = {BCCS-03-01},
PDF = {http://www.cs.bc.edu/~alvarez/ChiSquare/chi2tr.pdf}
}
%--- MQPs -----------------------------------
@misc{halwes.ea:-mqp:01,
author = {Tara Halwes},
title = {Transform-based Similarity Methods for Sequence Mining},
note = {Winner, Provost's MQP Award for Computer Science},
year = {2000},
month = {Aug.},
howpublished = {Undergraduate Graduation Project ({MQP}). Worcester Polytechnic Institute},
}
@misc{HL:financial-mqp:03,
author = {Sam Holmes and Cindy Leung},
title = {Exploring Temporal Associations in the Stock Market},
year = {2003},
month = {April},
howpublished = {Undergraduate Graduation Project ({MQP}). Worcester Polytechnic Institute},
}
@misc{zack:wekarminer-mqp:02,
author = {Zachary Stoecker-Sylvia},
title = {Merging the Association Rule Mining Modules of the Weka and ARMiner
Data Mining Systems},
year = {2002},
month = {April},
howpublished = {Undergraduate Graduation Project ({MQP}). Worcester Polytechnic Institute},
}
%----------------------------------------------------------------------------%
% MSTHESIS
@mastersthesis{Paramesh:msthesis,
address = {},
author = {P. Laxminarayan},
month = {},
school = {Department of Computer Science, Worcester Polytechnic Institute},
title = {Exploratory Analysis of Sleep Data},
year = {In progress},
}
@mastersthesis{Shoemaker:msthesis:2001,
address = {},
author = {C.A. Shoemaker},
month = {May},
school = {Department of Computer Science, Worcester Polytechnic Institute},
title = {Mining Association Rules from Set-Valued Data},
year = {2001},
}
%---------------------------------------------------------------------------%
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