Data mining-generation and visualisation of decision trees

• Autorzy: Kwaśnicka H. , Doczekalski M.
• Języki publikacji: EN

Abstrakty

EN

A computer system presented in the paper is developed as a data mining tool-it allows using large databases as a source for the process of decision tree generation and visualisation. The designed system (DTB&V-Decision Tree Builder and Visualiser) is able to perform data preprocessing, generation of decision trees followed by their post-processing and visualisation. DTB&V was tested using a number of databases commonly employed for such tasks.

Słowa kluczowe

EN

data mining tool; large databases; decision tree generation; decision tree visualisation; data pre-processing; data post-processing; knowledge discovery in databases; decision making; presentation layer; pseudocode

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2002
• Tom: Vol. 28, nr 3
• Strony: 63--84
• Opis fizyczny: Bibliogr. 41 poz.

Twórcy

autor

Kwaśnicka H.

• Department of Computer Science, Wrocław, Universit of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Doczekalski M.

• Department of Computer Science, Wrocław, Universit of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• [1] ADRIAANS P., DOLFZ., Data Mining, Addison-Wesley, 1996.
• [2] AGRAVAL R., GHOSH S., IMIELINSKI T., IYER B., SWAMI A., An Interval Classifier for Database Mining Applications, VLDB 92, Vancouver 1992, 560-573.
• [3] AGRAVAL R., IMIELINSKI T., SWAMI A., Data mining: A performance perspective, IEEE Transactions on Knowledge and Data Engineering, 1993.
• [4] AGRAVAL R., IMIELINSKI T., SWAMI A., Mining association rules between sets of items in large data­ bases, Proceedings of the 1993 International Conference on Management of Data, 1993.
• [5] AGRAVAL R., SRIKANT R., Fast algorithms for mining association rules in large databases, VLDB ’94, 1994.
• [6] AGRAVAL R., SRIKANT R., Mining Sequential Patterns: Generalisations and Performance Improvements, Research Report, IBM Almaden Research Center.
• [7] AGRAVAL R., SRIKANT R., Mining Sequential Patterns, Research Report, IBM Almaden Research Center.
• [8] CICHOSZ P., Systemy uczące się, (Machine Learning Systems), WNT Warsaw, 2000.
• [9] Database TCPIP - http://kdd.ics.uci.edu/databases/kddcup99.
• [10] Databases STATLOG - http://www.ics.uci.edu/~mlearn/MLRepository.html.
• [11] DOCZEKALSKI M., Techniki pozyskiwania wiedzy z baz danych, (Data Mining Techniques), Master Thesis, Department of Computer Science, Wrocław 2001.
• [12] FAYYAD U. M., PIATETSKY-SHAPIRO G., SMYTH P., UTHURUSAMY R., Advances in Knowledge Discovery and Data Mining, Cambridge MA, 1996.
• [13] GORAWSKI M., Data Warehouse: Systemy Wspomagania Podejmowania Decyzji, (Decision Support Systems), Software 5/99.
• [14] GORAWSKI M., FRĄCZAK J., Data Warehouse: Analiza porównawcza MOLAP i ROLAP, (Comparison of MOLAP and ROLAP), Software 8/99.
• [15] GORAWSKI M., FRĄCZAK J., Data Warehouse: Modelowanie danych, (Data modeling), Software 7/99.
• [16] GORAWSKI M., KONOPACKI A., Data Warehouse: Architektura, (Architecture), Software 6/99. [17] GORAWSKI M., KOZIATEK A., Data Warehouse: Ekstrakcja danych, (Data extraction), Software 9/99
• [18] GRABMEIER J„ RUDOLPH A., Techniques of Cluster Algorithms in Data Mining version 2.0, IBM Informationssysteme GmbH, 1998.
• [19] HOLSHEIMER M., SIEBES A., Data Mining. The Search for Knowledge in Databases, 1996.
• [20] HOUTSMA M., SWAMI A., Set-oriented mining of association rules, Research Report. IBM Almande Research Center, 1993.
• [21] IBM’s Data Mining Technology, White Paper, 1996.
• [22] Librery STŁPort wersja 3.2.1 - http://www.stlport.org.
• [23] MANNILA H., TOIVONEN H., VERKAMO I. A., Discovery of frequent episodes in event sequence University of Helsinki, 1997.
• [24] MANNILA H., TOIVONEN H., VERKAMO I. A., Efficient Algorithms for Discovering Association Rule AAAI Workshop on Knowledge Discovery in Databases, 1994.
• [25] MEHTA M., AGRAWAL R., RISANEN J., SLIQ: A fast scalable classifier for data mining, Proceeding of the Fifth International Conference on Extending Database Technology, Avignon 1996.
• [26] MEHTA M., RISSANEN J., AGRAWAL R., MDL-based decision tree pruning, International Conference on Knowledge Discovery in Databases and Data Mining, Montreal 1995.
• [27] MITCHELL T. M., Machine Learning, McGraw-Hill Series in Computer Science, 1997.
• [28] MICHIE D., SPIEGELHALTER D. J., TAYLOR C. C., Machine Learning, Neural and Statistical Classification, 1994.
• [29] NASA Ames Res, “Introduction to IND Version 2.1”, 1992.
• [30] PIATETSKY-SHAPIRO G., FRAWLEY W., Knowledge Discovery from Databases, Cambridge MA, 1991
• [31] Program MineSet 3.1, Silicon Graphics - http://www.sgi.com/software/mineset.
• [32] QUINLAN J. R., Induction of decision trees, Machine Learning, 1986.
• [33] RDBMS Interbase 6.0 OpenSource - http://www.inprise.com/interbase.
• [34] SHAFER J. C., AGRAVAL R., MEHTA M., SPRINT: A scalable parallel classifier for data mining Proceedings of the 1996 International Conference on Very Large Databases, Bombay 1996.
• [35]SRIKANT R„ AGRAVAL R„ Mining Generalized Association Rules, Proceedings of the 21st International Conference on Very Large Databases, September 1995.
• [36] SRIKANT R., VU Q., AGRAWAL R., Mining Association Rules with Item Constraints, AAAI, 1997. I [37] STOLFO J. S., FAN W., LEE W., PRODROMIDIS A., CHAN P. K., Cost-based Modeling and Evaluate for Data Mining With Application to Fraud and Intrusion Detection: Results from the JAM Project 1999.
• [38] TOIVONEN H., KLEMETTINEN M., RONKAINEN P., HATÓNEN K., MANNILA H., Pruning and Groupie Discovered Association Rules, University of Helsinki.
• [39] VENTURA D., On Discretisation as a Preprocessing Step for Supervised Learning Methods, Thesis presented to Department of Computer Science at Brigham Young University, 1995.
• [40] WALLACE C., PATRICK J., Coding decision trees, Machine Learning, 1993.
• [41] XIAOLIN WU, WITTEN IAN H., A Fast K-Means Type Clustering Algorithm, University of Calgary 1995.