Bibliographie
[1] R. Agrawal, T. Imielinski, an d A. Swami. Mining
association rules between sets of items in large databases. In Proceedings of
the ACM-SIGMOD Intl. Conference on Management of Data, Washington D. C., USA,
pages 207216, May 1993.
[2] R. Agrawal an d R. Srikant. Fast algorithms for mining
association rules. In J. B. Bocca, M. Jarke, an d C. Zaniolo, editors,
Proceedings of the 20th Intl. Conference on Very Large Databases, Santiago,
Chile, pages 478-499, June 1994.
[3] M. Barbut an d B. Monjar det. Ordre et classification.
Algèbre et Combinatoire. Hachette, Tome II, 1970.
[4] Y. Basti de. Data mining : algorithmes par niveau,
techniques d,implantation et applications. These de doctorat, Ecole
Doctorale Sciences pour l,Ingénieur de Clermont-Ferrand,
Université Blaise Pascal, France, Décembre 2000.
[5] Y. Basti de, N. Pasquier, R. Taouil, L. Lakhal, an d G.
Stumme. Mining minimal non-redundant association rules using frequent close d
itemsets. In Proceedings of the International Conference DOOD'2000,
Springer-Verlag, LNAI, volume 1861, London, UK, pages 972986, July 2000.
[6] Y. Basti de, R. Taouil, N. Pasquier, G. Stumme, an d L.
Lakhal. Mining frequent patterns with counting inference. The Sixth ACM-SIGKDD
International Conference on Knowledge Discovery and Data Mining, Boston,
Massachusetts, USA, 2(2) :6675, August 2023, 2000.
[7] S. BenYahia, C. L. Cherif, G. Mineau, an d A. Jaoua.
Découverte des regles associatives non re don dantes : application aux
corpus textuels. Revue d'Intelligence Artificielle (special issue of Intl.
Conference of Journées francophones d'Extraction et Gestion des
Connaissances (EGC'2003)), Lyon, France, 17(123) :131143, 2224 Janvier 2003.
[8] S. BenYahia, N. Doggaz, Y. Slimani, an d J. Rezgui. A
Galois connection semantics-based approach for deriving generic bases of
association rules. Revue d'Intelligence Artificielle (special issue of Intl.
Conference of Journées francophones d'Extraction et Gestion des
Connaissances (EGC'2004)), Clermont-Ferrand, France, 17(1) :131143, 2124
Janvier 2004.
[9] S. BenYahia an d E. Mephu Nguifo. Approches
d,extraction de regles d,association basées sur la
correspon dance de Galois. In J.-F. Boulicault an d B. Cremilleux, editors,
Revue
d'Ingénierie des Systêmes d'Information (ISI),
Hermês-Lavoisier, volume 9, pages 2355, 2004.
[10] S. BenYahia an d E. Mephu Nguifo. Revisiting generic
bases of association rules. In Proceedings of 6th International Conference on
Data Warehousing and Knowledge Discovery (DaWaK 2004), LNCS 3181,
Springer-Verlag, Zaragoza, Spain, pages 58-67, September 13, 2004.
[11] M. J. A. Berry an d G. S. Linoff. Data Mining Techniques :
For Marketing, Sales, and Customer Relationship Management, Second Edition.
Wiley Publishing, 2004.
[12] F. Bo don. A fast apriori implementation. In B. Goethals
an d M. J. Zaki, editors, Proceedings of the IEEE ICDM Workshop on Frequent
Itemset Mining Implementations (FIMI'03), volume 90 of CEUR Workshop
Proceedings, Melbourne, Florida, USA, November 19, 2003.
[13] F. Bo don. Surprising results of trie-base d fim
algorithms. In B. Goethals, M. J. Zaki, and R. Bayar do, editors, Proceedings
of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI'04),
volume 126 of CEUR Workshop Proceedings, Brighton, UK, November 1st 2004.
[14] F. Bo don an d L. Rányai. Trie : An alternative
data structure for data mining algorithms. Journal of Hungarian Applied
Mathematics and Computer Application, 38(7-9) :739751, October 2003.
[15] F. Bonchi an d C. Lucchese. On close d constraine d
frequent pattern mining. In Proceedings of the Fourth IEEE International
Conference on Data Mining (ICDM'04), Brighton, UK, pages 3542, November 14,
2004.
[16] J.-F. Boulicault an d B. Crémilleux. Editorial du
volume. In J.-F. Boulicault an d B. Cremilleux, editors, Revue
d'Ingénierie des Systêmes d'Information (ISI),
Hermês-Lavoisier, volume 9, pages 722, 2004.
[17] T. Brijs, G. Swinnen, K. Vanhoof, an d G. Wets. Using
association rules for product as-
sortment decisions : A case study. In Proceedings of the sixth
ACM-SIGKDD International
Conference on Knowledge Discovery and Data Mining, San Diego,
California, USA, pages
254260, August 1518 1999.
[18] T. Cal ders an d B. Goethals. Mining all non-derivable
frequent itemsets. In T. Elomaa, H. Mannila, an d H. Toivonen, editors,
Proceedings of the 6th European Conference on Principles of Data Mining and
Knowledge Discovery, PKDD 2002, LNCS, volume 2431, Springer-Verlag, Helsinki,
Finland, pages 7485, August 1923 2002.
[19] W. Cheung an d O.R. Zaiane. Incremental mining of
frequent patterns without candidate generation or support constraint. In
Proceedings of the Seventh International Database Engineering and Applications
Symposium (IDEAS 2003), Hong Kong, China, pages 111 116, July 1618, 2003.
[20] C. Creighton an d S. Hanash. Mining gene expression
databases for association rules. In Journal Bioinformatics, volume 19, pages
79-86, 2003.
[21] B.A Davey an d H.A. Priestley. Introduction to Lattices and
Order. Cambridge University Press, 2002.
[22] G. Dong, C. Jiang, J. Pei, J. Li, an d L. Wong. Mining
succinct systems of minimal generators of formal concepts. In Proceedings of
10th International Conference on Database Systems for Advanced Applications
(DASFAA 2005), Beijing, China, pages 175187, April 2005.
[23] A. Le Floc,h, C. Fisette, R. Missaoui, P.
Valtchev, an d R. Go din. JEN : un algorithme efficace de construction de
générateurs pour l,i dentification des regles
d,association. Numéro spécial de la revue des
Nouvelles Technologies de l'Information, 1(1) :135-146, 2003.
[24] J. M. Franco. Le Data Warehouse Le Data Mining.
Eyrolles, 1999.
[25] W. J. Frawley, G. Piatetsky-Shapiro, an d C. J. Matheus.
Knowledge discovery in databases - an overview. Artificial Intelligence
Magazine, 13 :5770, 1992.
[26] H. Fu an d E. Mephu Nguifo. Etude et conception
d,algorithmes de génération de concepts formels. In
J.-F. Boulicault an d B. Crémilleux, editors, Revue d'Ingénierie
des Systêmes d'Information (ISI), Hermês-Lavoisier, volume 9, pages
109-132, 2004.
[27] B. Ganter an d R. Wille. Formal Concept Analysis.
Springer-Verlag, 1999.
[28] K. Geurts. Traffic accidents data set. In B. Goethals an
d M. J. Zaki, editors, Proceedings of the IEEE ICDM Workshop on Frequent
Itemset Mining Implementations (FIMI'03), volume 90 of CEUR Workshop
Proceedings, Melbourne, Florida, USA, November 19, 2003.
[29] R. Go din, R. Missaoui, an d H. Alaoui. Incremental
concept formation algorithms base don Galois (concept) lattices. Journal of
Computational Intelligence, 11(2) :246267, May 1995.
[30] G. Grahne an d J. Zhu. Efficiently using prefix-trees in
mining frequent itemsets. In B. Goethals an d M. J. Zaki, editors, Proceedings
of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI'03),
volume 90 of CEUR Workshop Proceedings, Melbourne, Florida, USA, November 19,
2003.
[31] J. L. Guigues an d V. Duquenne. Familles minimales
d,implications informatives résultant d,un tableau
de données binaires. Mathématiques et Sciences Humaines, 24(95)
:518, 1986.
[32] T. Hamrouni, S. BenYahia, an d Y. Slimani. PRINCE :
Extraction optimisée des bases génériques de regles sans
calcul de fermetures. In Proceedings of the 23rd International Conference
INFORSID, Inforsid Editions, Grenoble, France, pages 353368, 2427 Mai 2005.
[33] T. Hamrouni, S. BenYahia, an d Y. Slimani. PRINCE : An
algorithm for generating rule bases without closure computations. In A Min Tjoa
an d J. Trujillo, editors, Proceedings of the 7th International Conference on
Data Warehousing and Knowledge Discovery (DaWaK
2005), Springer-Verlag, LNCS, volume 3589, Copenhagen, Denmark,
pages 346355, August 22-26, 2005.
[34] J. Han, J. Pei, an d Y. Yin. Mining frequent patterns
without candidate generation. In Proceedings of the ACM-SIGMOD Intl. Conference
on Management of Data (SIGMOD'00), Dallas, Texas, USA, pages 112, May 2000.
[35] J. Han, J. Pei, Y. Yin, an d R. Mao. Mining frequent
patterns without candidate generation : A frequent-pattern tree approach. In
Data Mining and Knowledge Discovery, volume 8, pages 53-87, 2004.
[36] J. Hipp, U. Giintzer, an d G. Nakhaeiza deh. Algorithms
for association rule mining - a general survey an d comparison. In ACM-SIGKDD
Explorations, New York, USA, volume 2, pages 5864, July 2000.
[37] W. A. Koesters an d W. Pijls. APRIORI, a depth first
implementation. In B. Goethals and M. J. Zaki, editors, Proceedings of the IEEE
ICDM Workshop on Frequent Itemset Mining Implementations (FIMI'03), volume 90
of CEUR Workshop Proceedings, Melbourne, Florida, USA, November 19, 2003.
[38] M. Kryszkiewicz. Representative association rules an d
minimum condition maximum conse-
quence association rules. In Proceedings of Second European
Symposium on Principles
of Data Mining and Knowledge Discovery (PKDD), 1998, LNCS,
volume 1510, Springer-
Verlag, Nantes, France, pages 361369, 1998.
[39] M. Kryszkiewicz. Concise representation of frequent
patterns base don disjunction-free generators. In Proceedings of the 1st IEEE
International Conference on Data Mining (ICDM), San Jose, California, USA,
pages 305312, 2001.
[40] M. Kryszkiewicz. Concise representation of frequent
patterns an d association rules. Habilitation thesis, Institute of Computer
Science, Warsaw University of Technology, Poland, August 2002.
[41] M. Kryszkiewicz. Concise representations of association
rules. In D. J. Hand, N.M. Adams, an d R.J. Bolton, editors, Proceedings of
Exploratory Workshop on Pattern Detection and Discovery in Data Mining (ESF),
Springer-Verlag, LNAI, volume 2447, London, UK, pages 92109, 2002.
[42] R. Lefebure an d G. Venturi. Le Data Mining. Eyrolles,
1999.
[43] C. Lucchese, S. Orlando, an d R. Perego. Mining frequent
close d itemsets without duplicates generation. Technical Report 13,
I.S.T.I.-C.N.R., Italy, 2004.
[44] C. Lucchesse, S. Orlando, an d R. Perego. DCI-CLOSED : a
fast an d memory efficient algorithm to mine frequent close d itemsets. In B.
Goethals, M. J. Zaki, an d R. Bayar do,
editors, Proceedings of the IEEE ICDM Workshop on Frequent
Itemset Mining Implementations (FIMI'04), volume 126 of CEUR Workshop
Proceedings, Brighton, UK, November 1st 2004.
[45] M. Luxenburger. Implications partielles dans un contexte.
Mathematiques et Sciences Humaines, 29(113) :35-55, 1991.
[46] A. M. Mueller. Fast sequential an d parallel algorithms
for association rules mining : a comparison. Technical Report CS-TR-3515,
Faculty of the Graduate School of the University of Maryland-College Park, USA,
October 1995.
[47] N. Pasquier. Datamining : Algorithmes
d,extraction et de reduction des regles d,association
dans les bases de donnees. These de doctorat, Ecole Doctorale Sciences pour
l,Ingenieur de Clermont Ferran d, Universite Clermont Ferran d II,
France, Janvier 2000.
[48] N. Pasquier, Y. Basti de, R. Taouil, an d L. Lakhal.
Pruning close d itemset lattices for association rules. In M. Bouzeghoub,
editor, Proceedings of 14th Intl. Conference Bases de Donnees Avancees,
Hammamet, Tunisia, pages 177196, October 2630, 1998.
[49] N. Pasquier, Y. Basti de, R. Taouil, an d L. Lakhal.
Efficient mining of association rules using close d itemset lattices. Journal
of Information Systems, 24(1) :25-46, 1999.
[50] N. Pasquier, Y. Basti de, R. Touil, an d L. Lakhal.
Discovering frequent close d itemsets for association rules. In C. Beeri an d
P. Buneman, editors, Proceedings of 7th International Conference on Database
Theory (ICDT'99), LNCS, volume 1540, Springer-Verlag, Jerusalem, Israel, pages
398416, January 1999.
[51] J. Pei, J. Han, an d R. Mao. CLOSET : An efficient
algorithm for mining frequent closed itemsets. In Proceedings of the ACM-SIGMOD
DMKD'00, Dallas, Texas, USA, pages 2130, 2000.
[52] J. L. Pfaltz an d C. M. Taylor. Scientific knowledge
discovery through iterative transformation of concept lattices. In Proceedings
of Workshop on Discrete Applied Mathematics in conjunction with the 2nd SIAM
International Conference on Data Mining, Arlington, Virginia, USA, pages 6574,
2002.
[53] A. Pietracaprina an d D. Zon dolin. Mining frequent
itemset using Patricia Tries. In B. Goethals an d M. J. Zaki, editors,
Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining
Implementations (FIMI'03), volume 90 of CEUR Workshop Proceedings, Melbourne,
Florida, USA, November 19, 2003.
[54] A. Salleb. Recherche de motifs frequents pour
l,extraction de regles d,association et de
caracterisation. These de doctorat, Laboratoire d,Informatique Fon
damentale d,Orleans LIFO, Université
d,Orléans, France, Décembre 2003.
[55] G. Stumme, R. Taouil, Y. Basti de, N. Pasquier, an d L.
Lakhal. Fast computation of concept lattices using data mining techniques. In
M. Bouzeghoub, M. Klusch, W. Nutt, an d U. Sattler, editors, Proceedings of 7th
Intl. Workshop on Knowledge Representation Meets Databases (KRDB'00), Berlin,
Germany, pages 129-139, 2000.
[56] G. Stumme, R. Taouil, Y. Basti de, N. Pasquier, an d L.
Lakhal. Intelligent structuring and reducing of association rules with formal
concept analysis. In F. Baader., G. Brewker, and T. Eiter, editors, Proceedings
of KI'2001 conference, LNAI, volume 2174, Springer-Verlag, Heidelberg, Germany,
2001.
[57] G. Stumme, R. Taouil, Y. Basti de, N. Pasquier, an d L.
Lakhal. Computing iceberg concept lattices with TITANIC. Journal on Knowledge
and Data Engineering (KDE), 2(42) :189222, 2002.
[58] R. Taouil. Algorithmique du treillis des fermes :
application a l,analyse formelle de concepts et aux bases de
donnees. These de doctorat, Ecole Doctorale Sciences pour
l,Ingenieur de Clermont?Ferran d, Universite Blaise Pascal, France,
Janvier 2000.
[59] H. Toivonen. Sampling large databases for association
rules. In Proceedings of the 22th Intl. Conference on Very Large Databases,
Bombay, India, pages 134145, September 1996.
[60] T. Uno, T. Asai, Y. Uchida, an d H. Arimura. LCM : An
efficient algorithm for enumerating
frequent close d item sets. In B. Goethals an d M. J. Zaki,
editors, Proceedings of the IEEE
ICDM Workshop on Frequent Itemset Mining Implementations
(FIMI'03), volume 90 of
CEUR Workshop Proceedings, Melbourne, Florida, USA, November 19,
2003.
[61] T. Uno, T. Asai, Y. Uchida, an d H. Arimura. An
efficient algorithm for enumerating closed patterns in transaction databases.
Journal of Discovery Science, LNAI, volume 3245, pages 16-31, 2004.
[62] T. Uno, M. Kiyomi, an d H. Arimura. LCM ver. 2 :
Efficient mining algorithms for
frequent/closed/maximal itemsets. In B. Goethals, M. J. Zaki, an
d R. Bayar do, editors,
Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining
Implementations
(FIMI'04), volume 126 of CEUR Workshop Proceedings, Brighton,
UK, November 1st, 2004.
[63] P. Valtchev, R. Missaoui, R. Go din, an d M. Meri dji.
Generating frequent itemsets incrementally : two novel approaches base don
Galois lattice theory. Journal Expt. Theoretical Artificial Intelligence, 14(1)
:115142, 2002.
[64] P. Valtchev, R. Missaoui, an d P. Lebrun. A fast
algorithm for building the Hasse diagram of a Galois lattice. In Proceedings of
the Colloque LaCIM 2000, Montreal, Canada, pages 293306, September 2000.
[65] J. Wang, J. Han, an d J. Pei. CLOSET+ : Searching for
the best strategies for mining frequent close d itemsets. In Proceedings of the
Ninth ACM-SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD'03), Washington D. C.,
USA, pages 236245, August 24-27, 2003.
[66] R. Wille. Restructuring lattices theory : An approach base
don hierarchies of concepts. I. Rival, editor, Ordered Sets, Dordrecht-Boston,
pages 445470, 1982.
[67] M. J. Zaki. Mining non-redundant association rules. In Data
Mining and Knowledge Discovery, volume 9, pages 223-248, 2004.
[68] M. J. Zaki an d K. Gouda. Fast vertical mining using
Diffsets. Technical report, Computer Science Dept., Rensselaer Polytechnic
Institute, USA, March 2001.
[69] M. J. Zaki an d C. J. Hsiao. CHARM : An efficient
algorithm for close d itemset mining. In Proceedings of the 2nd SIAM
International Conference on Data Mining, Arlington, Virginia, USA, pages 34-43,
April 2002.
[70] M. J. Zaki, S. Parthasarathy, M. Ogihara, an d W. Li.
New algorithms for fast discovery of association rules. In D. Heckerman, H.
Mannila, an d D. Pregibon, editors, Proceedings of the 3rd International
Conference on Knowledge Discovery and Data Mining (KDD '97), Newport Beach,
California, USA, pages 283290, August 1997.
|