Article

Discovering the set of fundamental rule changes

Authors:

Yiming MaAuthors Info & Claims

KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 335 - 340

https://doi.org/10.1145/502512.502561

Published: 26 August 2001 Publication History

Abstract

The world around us changes constantly. Knowing what has changed is an important part of our lives. For businesses, recognizing changes is also crucial. It allows businesses to adapt themselves to the changing market needs. In this paper, we study changes of association rules from one time period to another. One approach is to compare the supports and/or confidences of each rule in the two time periods and report the differences. This technique, however, is too simplistic as it tends to report a huge number of rule changes, and many of them are, in fact, simply the snowball effect of a small subset of fundamental changes. Here, we present a technique to highlight the small subset of fundamental changes. A change is fundamental if it cannot be explained by some other changes. The proposed technique has been applied to a number of real-life datasets. Experiments results show that the number of rules whose changes are unexplainable is quite small (about 20% of the total number of changes discovered), and many of these unexplainable changes reflect some fundamental shifts in the application domain.

References

[1]

Agrawal, R. and Psaila, G. "Active data mining." KDD-95.

[2]

Agrawal, R. and Srikant, R. "Fast algorithms for mining association rules." VLDB-94, 1994.

Digital Library

[3]

Bayardo, R., Agrawal, R, and Gunopulos, D. "Constraintbased rule mining in large, dense databases." ICDE-99, 1999.

[4]

Cheung, D. Han, J, V. Ng, and Wong, C.Y. "Maintenance of discovered association rules in large databases: an incremental updating technique." ICDE-96, 1996.

Digital Library

[5]

Dong, G. and J. Li. "Efficient mining of emerging patterns: discovering trends and differences." KDD-99, 1999.

Digital Library

[6]

Ester, M., Frommelt, A., Kriegel, H-P., and Sander, J. "Algorithms for characterization and trend detection in spatial databases." KDD-98, 1998.

[7]

Fayyad, U. M. and Irani, K. B. "Multi-interval discretization of continuous-valued attributes for classification learning." IJCAI-93, 1993.

[8]

Ganti, V., Gehrke, J., and Ramakrishnan, R. "A framework for measuring changes in data characteristics." POPS-99, 1999.

Digital Library

[9]

Ganti, V., Gehrke, J., and Ramakrishnan, R. "DEMON: mining and monitoring evolving data." ICDE-2000, 2000.

[10]

Han, J. and Fu, Y. "Discovery of multiple-level association rules from large databases." VLDB-95, 1995.

Digital Library

[11]

Johnson T. and Dasu, T. "Comparing massive highdimensional data sets," KDD-98, 1998.

[12]

Kohavi, R., John, G., Long, R., Manley, D., and Pfleger, K. "MLC++: a machine learning library in C++." Tools with artificial intelligence, 740-743, 1994.

[13]

Lane, T. and Brodley, C. "Approaches to online learning and concept drift for user identification in computer security." KDD-98, 1998.

[14]

Liu, B., Hsu, W., and Chen, S. "Using general impressions to analyze discovered classification rules." KDD-97, 1997.

[15]

Liu, B., Hsu, W. and Ma, Y. Discovering the set of fundamental rule changes. NUS Technical Report, 2001.

[16]

Liu, B., Hsu, W. and Ma, Y. "Pruning and summarizing the discovered associations." KDD-99, 1999.

Digital Library

[17]

Mann, P. Introductory statistics. John Wiley & Sons, 1998.

[18]

Padmanabhan, B., and Tuzhilin, A. "Small is beautiful: Discovering the minimal set of unexpected patterns." KDD- 2OO0, 2000.

Digital Library

[19]

Silberschatz, A., and Tuzhilin, A. "What makes patterns interesting in knowledge discovery systems." IEEE Trans. on Know. and Data Eng. 8(6), 1996.

Digital Library

[20]

Widmer, G. "Learning in the presence of concept drift and hidden contexts." Machine learning, 23:69-101, 1996.

Cited By

Pätzel DNordsieck RHähner J(2024)Measuring Similarities in Model Structure of Metaheuristic Rule Set LearnersApplications of Evolutionary Computation10.1007/978-3-031-56855-8_16(256-272)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56855-8_16
Meißner KRüther CAmbrosi K(2018)Detecting Changes in Statistics of Road Accidents to Enhance Road SafetyOperations Research Proceedings 201710.1007/978-3-319-89920-6_10(67-72)Online publication date: 26-May-2018
https://doi.org/10.1007/978-3-319-89920-6_10
Pei J(2018)Association RulesEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_26(182-184)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_26
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Index Terms

Discovering the set of fundamental rule changes
1. Applied computing
  1. Enterprise computing
    1. Business process management
2. Information systems
  1. Data management systems
    1. Database management system engines
      1. Triggers and rules
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

August 2001

493 pages

ISBN:158113391X

DOI:10.1145/502512

Conference Chair:
Doheon Lee
Chonnam National University, Korea
,
General Chair:
Mario Schkolnick
SGI
,
Program Chairs:
Foster Provost
New York University
,
Ramakrishnan Srikant
IBM Almaden Research Center

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI: American Association for Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2001

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KDD01

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KDD01: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 26 - 29, 2001

California, San Francisco

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KDD '01 Paper Acceptance Rate 31 of 237 submissions, 13%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Pätzel DNordsieck RHähner J(2024)Measuring Similarities in Model Structure of Metaheuristic Rule Set LearnersApplications of Evolutionary Computation10.1007/978-3-031-56855-8_16(256-272)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56855-8_16
Meißner KRüther CAmbrosi K(2018)Detecting Changes in Statistics of Road Accidents to Enhance Road SafetyOperations Research Proceedings 201710.1007/978-3-319-89920-6_10(67-72)Online publication date: 26-May-2018
https://doi.org/10.1007/978-3-319-89920-6_10
Pei J(2018)Association RulesEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_26(182-184)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_26
Dong GLi J(2018)Emerging PatternsEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_145(1294-1297)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_145
Fassetti FRombo SSerrao CFassetti FRombo SSerrao C(2017)Exceptional Pattern DiscoveryDiscriminative Pattern Discovery on Biological Networks10.1007/978-3-319-63477-7_3(23-30)Online publication date: 2-Sep-2017
https://doi.org/10.1007/978-3-319-63477-7_3
Pei J(2017)Association RulesEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_26-2(1-3)Online publication date: 22-Aug-2017
https://doi.org/10.1007/978-1-4899-7993-3_26-2
Dong GLi J(2017)Emerging PatternsEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_145-2(1-3)Online publication date: 4-Jan-2017
https://doi.org/10.1007/978-1-4899-7993-3_145-2
(2014)A Tree-Based Contrast Set-Mining Approach to Detecting Group DifferencesINFORMS Journal on Computing10.1287/ijoc.2013.055826:2(208-221)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1287/ijoc.2013.0558
Cagliero L(2013)Discovering Temporal Change Patterns in the Presence of TaxonomiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2011.23325:3(541-555)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1109/TKDE.2011.233
Saxena PPant BGoudar RSrivastav SGarg VPareek S(2013)Future predictions in Indian stock market through linguistic-temporal approach2013 7th International Conference on Intelligent Systems and Control (ISCO)10.1109/ISCO.2013.6481191(416-420)Online publication date: Jan-2013
https://doi.org/10.1109/ISCO.2013.6481191
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