[AGGR98] R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proceedings of the ACM-SIGMOD 1998

(1)

Bibliografia

[ABKS99] M. Ankerst, M. M. Breunig, H. P. Kriegel and J. Sander. OPTICS:

Ordering Points to Identify the Clustering Structure. In Proceedings of the ACM-SIGMOD 1999 International Conference on

Management of Data, 49-60, Philadelphia, PA., 1999.

[AGGR98] R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proceedings of the ACM-SIGMOD 1998

International Conference on Management of Data, 94-105, Seattle, WA., 1998.

[AIS93] R. Agrawal, T. Imielinski and A. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data, 207-216, Washington, D.C., 1993.

[AS94] R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Databases (VLDB), 487- 499, Santiago del Chile, Chile, 1994.

[AS95] R. Agrawal and R. Srikant. Mining Sequential Patterns. In Proceedings of the 11th International Conference on Data Engineering (ICDE), 3-14, Taipei, Taiwan, 1995.

[BL97] M. J. Berry and G. Linoff. Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley & Sons, Inc., New York.

1997.

[Cha03] C. Chatfield. The Analysis of Time Series. 6

^th

edition. Chapman &

Hall/CRC. Boca Raton, FL., 2003.

[CD97] S. Chaudhuri and U. Dayal. An Overview of Data Warehousing and OLAP Technology. ACM-SIGMOD Record, 26 (1): 65-74, 1997.

[DELPHI] Delphi Borland.

(http://www.borland.com/us/products/delphi/index.html) © Borland Software Corporation. 1994-2005.

[DMG] The Data Mining Group. (http://www.dmg.org/pmml-v2-0.html).

2003.

(2)

[DZ04] J. Demsar and B. Zupan. Orange: From Experimental Machine Learning to Interactive Data Mining. White Paper.

(http://www.ailab.si/orange/doc), Faculty of Computer and Information Science, University of Ljubljana, Slovenia, 2004.

[EKSX96] M. Ester, H. P. Kriegel, J. Sander and X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, (KDD’96), 226-231, Portland, OR., 1996.

[FCK94] K. H. Fasman, A. J. Cuticchia, and D. T. Kingsbury. The GDB Human Genome Data Base. Nucleic Acids Research, 22 (17): 3462- 3469, 1994.

[FGM] Fujitsu GhostMiner.

(http://www.fqspl.com.pl/?a=product_view&id=2&lang=en&x=) © FQS Poland, 2002-2005.

[Fis36] R. A. Fisher. The Use of Multiple Measurements in Taxonomic Problems. Annual Eugenics, 7: 179-188, 1936. Also in Contributions to Mathematical Statistics. John Wiley & Sons, Inc., New York, 1950.

[Fis87] D. H. Fisher. Improving Inference through Conceptual Clustering. In Proceedings of AAAI-87 Sixth National Conference on Artificial Intelligence, 461-465, Seattle, WA., 1987.

[FPSSU96] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy.

Advances in Knowledge Discovery and Data Mining. AAAI Press / The MIT Press, Cambridge, MA., 1996.

[GLF89] J. H. Gennari, P. Langley and D. Fisher. Models of Incremental Concept Formation. Journal of Artificial Intelligence, 40: 11-61, 1989.

[GRS98] S. Guha, R. Rastogi and K. Shim. CURE: An Efficient Clustering Algorithm for Large Databases. In Proceedings of the ACM-SIGMOD 1998 International Conference on Management of Data, 73-84, Seattle, WA., 1998.

[Har01] E. R. Harold. XML Bible. 2

^nd

edition. John Wiley & Sons, Inc. New York, 2001

[Hed96] S. R. Hedberg. Searching for the Mother Lode: Tales of the First Data

Miners. IEEE Expert: Intelligent Systems and Their Applications, 11,

(5):4-7, 1996.

(3)

[HF95] J. Han and Y. Fu. Discovery of Multiple-level Association Rules from Large Databases. In Proceedings of the 21st International Conference on Very Large Databases (VLDB), 420-431, Zurich, Switzerland, 1995.

[HK98] A. Hinneburg and D. A. Keim. An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, (KDD’98), 58-65, New York, 1998.

[HK00] J. Han and M. Kamber. Data Mining: Concepts and Techniques.

Morgan Kaufmann. San Francisco, CA., 2000.

[IM96] T. Imielinski and H. Mannila. A Database Perspective on Knowledge Discovery. Communications of the ACM, 39 (11): 58-64, 1996.

[KDN] KDnuggets. Software Suites for Data Mining and Knowledge Discovery. (http://www.kdnuggets.com/software/suites.html). © KDnuggets. 2005.

[KR90] L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Inc. New York, 1990.

[Mac67] J. MacQueen. Some Methods for Classification and Analysis of Multivariate Observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 281-297, Berkeley, CA., 1967.

[MPC96] R. Meo, G. Psaila and S. Ceri. A New SQL-like Operator for Mining Association Rules. In Proceedings of 22nd International Conference on Very Large Databases (VLDB), 122-133, Mumbai (Bombay), India, 1996.

[ORANGE] ORANGE. Data Mining Fruitful & Fun. (http://www.ailab.si/orange)

[ORS98] B. Özden, S. Ramaswamy and A. Silberschatz. Cyclic Association Rules. In Proceedings of the 14th International Conference on Data Engineering (ICDE), 412-421, Orlando, FL., 1998.

[PCY95] J. S. Park, M. S. Chen, and P. S. Yu. An Effective Hash-based

Algorithm for Mining Association Rules. In Proceedings of the ACM-

SIGMOD 1995 International Conference on Management of Data,

175-186, San Josè, CA., 1995.

(4)

[PERL] PERL. O’Reilly perl.com. The Source for Perl.

(http://www.perl.com/) © O’Reilly Media, Inc. All Rights Reserved.

2000-2005.

[PYTHON] PYTHON. (http://www.python.org) © Python Software Foundation.

2005.

[Qui86] J. R. Quinlan. Induction of Decision Trees. Machine Learning, 1 (1):

81-106, 1986.

[Qui93] J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann. San Francisco, CA., 1993.

[RRT05] A. Romei, S. Ruggeri and F.Turini. KDDML: a Middleware Language and System for Knowledge Discovery in Databases. In Proceedings of the 13th Italian Symposium on Advanced Database Systems (SEBD 2005), 208-219, Brixen-Bressanone, 2005.

[SCZ98] G. Sheikholeslami, S. Chatterjee and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th International Conference on Very Large Databases (VLDB), 428-439, New York, 1998.

[SON95] A. Savasere, E. Omiecinski and S. B. Navathe. An Efficient Algorithm for Mining Association Rules in Large Databases. In Proceedings of the 21st International Conference on Very Large Databases (VLDB), 432-444, Zurich, Switzerland, 1995.

[Str97] B. Stroustrup. The C++ Programming Language. 3

^rd

edition.

Addison Wesley Longman. Reading, MA., 1997.

[TAN] TANAGRA. A Free Data Mining Software for Research and Education. (http://eric.univ-lyon2.fr/~ricco/tanagra/). 2005.

[TCL] Tcl Developer Xchange (http://www.tcl.tk/).

[ZDKO+01] B. Zupan, J. Demsar, M. W. Kattan, M. Ohori, M. Graefen, M.

Bohanec and J. R. Beck. Orange and Decisions-at Hand: Bridging

Predictive Data Mining and Decision Support In C. Giraud-Carrier,

N. Lavrac and Steve Moyle (eds.) ECML/PKDD'01 Workshop on

Integrating Aspects of Data Mining, Decision Support and Meta-

Learning, 151-162, Freiburg, Germany, 2001.

(5)

[ZLDC04] B. Zupan, G. Leban, J. Demsar and T. Curk. Widgets and Visual Programming. White Paper. (http://www.ailab.si/orange/doc), Faculty of Computer and Information Science, University of Ljubljana, Slovenia, 2004.

[ZRL96] T. Zhang, R. Ramakrishnan and M. Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the ACM-SIGMOD 1996 International Conference on Management of Data, 103-114, Montreal, Canada, 1996.

[YALE] YALE. Yet Another Learning Environment.

(http://www-ai.cs.uni-dortmund.de/SOFTWARE/YALE/index.html)

[W3CXSL] eXtensible Stylesheet Language (XSL).

[WB98] C. Westphal and T. Blaxton. Data Mining Solutions. Methods and Tools for Solving Real-World Problems. John Wiley & Sons, Inc.

New York, 1998.

[WEKA] Weka 3: Data Mining Software in Java (http://www.cs.waikato.ac.nz/ml/weka/)

[WF00] I. H. Witten and E. Frank. Nuts and Bolts: Machine Learning Algorithms in Java. In Data Mining: Practical Learning Tools and Techniques with Java Implementations, 265-320, Morgan Kauffman.

San Francisco, CA., 2000.

[WPV] Weka PMML and Visualization.

(http://www.cs.bris.ac.uk/home/jl2092/index.html). Jiwen Li.

[WS91] J. Way and E. A. Smith. The Evolution of Synthetic Aperture Radar Systems and their Progression to the EOS SAR. IEEE Transactions on Geoscience and Remote Sensing, 29 (6): 962-985, 1991.

[WYM97] W. Wang, J. Yang and R. R. Muntz. STING: A Statistical

Information Grid Approach to Spatial Data Mining. In Proceedings of

the 23rd International Conference on Very Large Databases (VLDB),

186-195, Athens, Greece, 1997.

[AGGR98] R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proceedings of the ACM-SIGMOD 1998

Bibliografia

[ABKS99] M. Ankerst, M. M. Breunig, H. P. Kriegel and J. Sander. OPTICS:

Ordering Points to Identify the Clustering Structure. In Proceedings of the ACM-SIGMOD 1999 International Conference on

Management of Data, 49-60, Philadelphia, PA., 1999.

[AGGR98] R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proceedings of the ACM-SIGMOD 1998

International Conference on Management of Data, 94-105, Seattle, WA., 1998.

[AIS93] R. Agrawal, T. Imielinski and A. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data, 207-216, Washington, D.C., 1993.

[AS94] R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Databases (VLDB), 487- 499, Santiago del Chile, Chile, 1994.

[AS95] R. Agrawal and R. Srikant. Mining Sequential Patterns. In Proceedings of the 11th International Conference on Data Engineering (ICDE), 3-14, Taipei, Taiwan, 1995.

[BL97] M. J. Berry and G. Linoff. Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley & Sons, Inc., New York.

1997.

[Cha03] C. Chatfield. The Analysis of Time Series. 6

edition. Chapman &

Hall/CRC. Boca Raton, FL., 2003.

[CD97] S. Chaudhuri and U. Dayal. An Overview of Data Warehousing and OLAP Technology. ACM-SIGMOD Record, 26 (1): 65-74, 1997.

[DELPHI] Delphi Borland.

(http://www.borland.com/us/products/delphi/index.html) © Borland Software Corporation. 1994-2005.

[DMG] The Data Mining Group. (http://www.dmg.org/pmml-v2-0.html).

2003.

[DZ04] J. Demsar and B. Zupan. Orange: From Experimental Machine Learning to Interactive Data Mining. White Paper.

(http://www.ailab.si/orange/doc), Faculty of Computer and Information Science, University of Ljubljana, Slovenia, 2004.

[EKSX96] M. Ester, H. P. Kriegel, J. Sander and X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, (KDD’96), 226-231, Portland, OR., 1996.

[FCK94] K. H. Fasman, A. J. Cuticchia, and D. T. Kingsbury. The GDB Human Genome Data Base. Nucleic Acids Research, 22 (17): 3462- 3469, 1994.

[FGM] Fujitsu GhostMiner.

(http://www.fqspl.com.pl/?a=product_view&id=2&lang=en&x=) © FQS Poland, 2002-2005.

[Fis36] R. A. Fisher. The Use of Multiple Measurements in Taxonomic Problems. Annual Eugenics, 7: 179-188, 1936. Also in Contributions to Mathematical Statistics. John Wiley & Sons, Inc., New York, 1950.

[Fis87] D. H. Fisher. Improving Inference through Conceptual Clustering. In Proceedings of AAAI-87 Sixth National Conference on Artificial Intelligence, 461-465, Seattle, WA., 1987.

[FPSSU96] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy.

Advances in Knowledge Discovery and Data Mining. AAAI Press / The MIT Press, Cambridge, MA., 1996.

[GLF89] J. H. Gennari, P. Langley and D. Fisher. Models of Incremental Concept Formation. Journal of Artificial Intelligence, 40: 11-61, 1989.

[GRS98] S. Guha, R. Rastogi and K. Shim. CURE: An Efficient Clustering Algorithm for Large Databases. In Proceedings of the ACM-SIGMOD 1998 International Conference on Management of Data, 73-84, Seattle, WA., 1998.

[Har01] E. R. Harold. XML Bible. 2

edition. John Wiley & Sons, Inc. New York, 2001

[Hed96] S. R. Hedberg. Searching for the Mother Lode: Tales of the First Data

Miners. IEEE Expert: Intelligent Systems and Their Applications, 11,

(5):4-7, 1996.

[HF95] J. Han and Y. Fu. Discovery of Multiple-level Association Rules from Large Databases. In Proceedings of the 21st International Conference on Very Large Databases (VLDB), 420-431, Zurich, Switzerland, 1995.

[HK98] A. Hinneburg and D. A. Keim. An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, (KDD’98), 58-65, New York, 1998.

[HK00] J. Han and M. Kamber. Data Mining: Concepts and Techniques.

Morgan Kaufmann. San Francisco, CA., 2000.

[IM96] T. Imielinski and H. Mannila. A Database Perspective on Knowledge Discovery. Communications of the ACM, 39 (11): 58-64, 1996.

[KDN] KDnuggets. Software Suites for Data Mining and Knowledge Discovery. (http://www.kdnuggets.com/software/suites.html). © KDnuggets. 2005.

[KR90] L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Inc. New York, 1990.

[Mac67] J. MacQueen. Some Methods for Classification and Analysis of Multivariate Observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 281-297, Berkeley, CA., 1967.

[MPC96] R. Meo, G. Psaila and S. Ceri. A New SQL-like Operator for Mining Association Rules. In Proceedings of 22nd International Conference on Very Large Databases (VLDB), 122-133, Mumbai (Bombay), India, 1996.

[ORANGE] ORANGE. Data Mining Fruitful & Fun. (http://www.ailab.si/orange)

[ORS98] B. Özden, S. Ramaswamy and A. Silberschatz. Cyclic Association Rules. In Proceedings of the 14th International Conference on Data Engineering (ICDE), 412-421, Orlando, FL., 1998.

[PCY95] J. S. Park, M. S. Chen, and P. S. Yu. An Effective Hash-based

Algorithm for Mining Association Rules. In Proceedings of the ACM-

SIGMOD 1995 International Conference on Management of Data,

175-186, San Josè, CA., 1995.

[PERL] PERL. O’Reilly perl.com. The Source for Perl.

(http://www.perl.com/) © O’Reilly Media, Inc. All Rights Reserved.

2000-2005.

[PYTHON] PYTHON. (http://www.python.org) © Python Software Foundation.

2005.

[Qui86] J. R. Quinlan. Induction of Decision Trees. Machine Learning, 1 (1):

81-106, 1986.

[Qui93] J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann. San Francisco, CA., 1993.

[RRT05] A. Romei, S. Ruggeri and F.Turini. KDDML: a Middleware Language and System for Knowledge Discovery in Databases. In Proceedings of the 13th Italian Symposium on Advanced Database Systems (SEBD 2005), 208-219, Brixen-Bressanone, 2005.

[SCZ98] G. Sheikholeslami, S. Chatterjee and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th International Conference on Very Large Databases (VLDB), 428-439, New York, 1998.

[SON95] A. Savasere, E. Omiecinski and S. B. Navathe. An Efficient Algorithm for Mining Association Rules in Large Databases. In Proceedings of the 21st International Conference on Very Large Databases (VLDB), 432-444, Zurich, Switzerland, 1995.

[Str97] B. Stroustrup. The C++ Programming Language. 3

edition.

Addison Wesley Longman. Reading, MA., 1997.

[SUN] Sun Developer Network. (http://java.sun.com/). © Sun Microsystems, Inc. 1994-2005

[TAN] TANAGRA. A Free Data Mining Software for Research and Education. (http://eric.univ-lyon2.fr/~ricco/tanagra/). 2005.

[TCL] Tcl Developer Xchange (http://www.tcl.tk/).

[TQT] Trolltech QT. (http://www.trolltech.com/). © Trolltech®. 2005

[ZDKO+01] B. Zupan, J. Demsar, M. W. Kattan, M. Ohori, M. Graefen, M.

Bohanec and J. R. Beck. Orange and Decisions-at Hand: Bridging

Predictive Data Mining and Decision Support In C. Giraud-Carrier,

N. Lavrac and Steve Moyle (eds.) ECML/PKDD'01 Workshop on

Integrating Aspects of Data Mining, Decision Support and Meta-

Learning, 151-162, Freiburg, Germany, 2001.

[ZLDC04] B. Zupan, G. Leban, J. Demsar and T. Curk. Widgets and Visual Programming. White Paper. (http://www.ailab.si/orange/doc), Faculty of Computer and Information Science, University of Ljubljana, Slovenia, 2004.

[ZRL96] T. Zhang, R. Ramakrishnan and M. Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the ACM-SIGMOD 1996 International Conference on Management of Data, 103-114, Montreal, Canada, 1996.

[YALE] YALE. Yet Another Learning Environment.

(http://www-ai.cs.uni-dortmund.de/SOFTWARE/YALE/index.html)