Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the KDD process that aims at selecting and applying a particular data mining algorithm to extract an interesting and useful knowledge. It is highly expected that data mining methods will find interesting patterns according to some measures, from databases. It is of vital importance to define good measures of interestingness that would allow the system to discover only the useful patterns. Measures of interestingness are divided into objective and subjective measures. Objective measures are those that depend only on the structure of a pattern and which can be quantified by using statistical methods. While, subjective measures depend only on the subjectivity and understandability of the user who examine the patterns. These subjective measures are further divided into actionable, unexpected and novel. The key issues that faces data mining community is how to make actions on the basis of discovered knowledge. For a pattern to be actionable, the user subjectivity is captured by providing his/her background knowledge about domain. Here, we consider the actionability of the discovered knowledge as a measure of interestingness and raise important issues which need to be addressed to discover actionable knowledge.
[1] Adomavicius, G. & Tuzhilin, A. 1997. Discovery of Actionable Patterns
in Databases: The Action Hierarchy Approach. In Proceedings of the
Third International Conference of Knowledge Discovery & Data
Mining. The AAAI Press.
[2] A. S. Al-Hegami, V. Bhatnagar, and N. Kumar. Novelty Framework for
Knowledge Discovery in Databases . In Proceedings of 6th International
Conference on Data Warehousing and Knowledge Discovery.(DaWaK
2004), Spain, 2004.
[3] A. S. Al-Hegami. Subjective Measures and their Role in Data Mining
Process. In Proceedings of the 6th International Conference on
Cognitive System. New Delhi, India, 2004.
[4] Han, J. and Kamber, M. 2001. Data Mining: Concepts and Techniques.
1st Edition, Harcourt India Private Limited.
[5] He., Zenyou. & Xu., Xiaofei., & deng., Shengchum. Data Mining for
Actionable Knowledge: A Survey. Supported by the High TechnologyResearch
& Development Program of China ( No. 2003 AA4Z2170,
No. 2003 AA413021) and the IBM SUR Research Fund.
[6] Liu, B. & Hsu, W. & Lee, H-Y. & Mum, L-F 1996. Tuple Level Anaysis
for Identification of Interesting Rules. In Technical Report TRA5/95,
SoC, National University of Singapore, Singapore.
[7] Liu, B. & Hsu, W. & Chen, S. & Ma, Y. 2000. Analyzing the Subjective
Interestingness of Association Rules. IEEE Intelligent Systems.
[8] Padmanabhan, B. & Tuzhilin, A. 1997. Unexpectedness as a Measure of
Interestingness in Knowledge Discovery. Working paper # IS-97- Dept.
of Information Systems. Stern School of Business, NYU.
[9] Padmanabhan, B. & Tuzhilin, A. 1998. A Belief-Driven Method for
Discovering Unexpected Patterns. KDD-98.
[10] Padmanabhan, B. & Tuzhilin, A. 2000. Small is Beautiful: Discovering
the Minimal Set of Unexpected Patterns. KDD-2000.
[11] Ras, Z.W. & Tzacheva, A., In search for action rules of the lowest cost.
In "Monitoring, Security and Rescue Techniques in Multiagent Systems".
Advances in Soft Computing , Springer-Verlag, 2005, 261-272
[12] Ras, Z.W. & Wieczorkowska, A.. Action Rules : How to increase profit
of a company. In Principles of Data Mining and Knowledge Discovery,
(Eds. D. A. Zighed, J. Komorowski, J. Zytkow) Proceedings of
PKDD-00, Lyon, France, LNCS/LNAI, No. 1910, Springer-Verlag
2000, 587-592.
[13] Silberschatz, A. & Tuzihilin, A. 1995. On Subjective Measures of
Interestingness in Knowledge Discovery. In Proceedings of the 1st
International Conference on Knowledge Discovery and Data Mining.
[14] Silberschatz, A. & Tuzihilin, A.1996. What makes patterns Interesting
in Knowledge Discovery Systems. IEEE Trans. And Data Engineering.
Vol.5, no. 6.
[15] Suzuki, E. 1997. Autonomous Discovery of Reliable Exception Rules. In
Proceedings of the 3rd International Conference on Knowledge
Discovery and Data Mining. Newport Beach, CA, USA.
[16] Tsay, L.-S., & Ras, Z. W. (2004). Action Rules Discovery System DEAR,
Method and Experiments. In the Special Issue on Knowledge Discovery,
( Ed. Z. W. Ras), Journal of Experimental and Theoretical Artificial
Intelligence, Taylor & Francis.
[17] Tsay, L.-S., & Ras, Z. W. (2005). Action Rules Discovery System
DEAR2, Method and Experiments. In the Special Issue on Knowledge
Discovery, ( Ed. Z. W. Ras), Journal of Experimental and Theoretical
Artificial Intelligence, Taylor & Francis. Vol. 17, No.1-2, January-June
2005, 119-128.
[18] Wang, K., & Zhou, S., & Han. J. (1999). Profit Mining: From Patterns
to Actions. National Science and Engineering Research Council of
Canada.
[19] Yang, Q., & Yin, J. and Ling, X. C., & Chen, T. Postprocessing
Decision Trees to Extract Actionable Knowledge . In IEEE International
Conference on Data Mining (ICDM-03).
[1] Adomavicius, G. & Tuzhilin, A. 1997. Discovery of Actionable Patterns
in Databases: The Action Hierarchy Approach. In Proceedings of the
Third International Conference of Knowledge Discovery & Data
Mining. The AAAI Press.
[2] A. S. Al-Hegami, V. Bhatnagar, and N. Kumar. Novelty Framework for
Knowledge Discovery in Databases . In Proceedings of 6th International
Conference on Data Warehousing and Knowledge Discovery.(DaWaK
2004), Spain, 2004.
[3] A. S. Al-Hegami. Subjective Measures and their Role in Data Mining
Process. In Proceedings of the 6th International Conference on
Cognitive System. New Delhi, India, 2004.
[4] Han, J. and Kamber, M. 2001. Data Mining: Concepts and Techniques.
1st Edition, Harcourt India Private Limited.
[5] He., Zenyou. & Xu., Xiaofei., & deng., Shengchum. Data Mining for
Actionable Knowledge: A Survey. Supported by the High TechnologyResearch
& Development Program of China ( No. 2003 AA4Z2170,
No. 2003 AA413021) and the IBM SUR Research Fund.
[6] Liu, B. & Hsu, W. & Lee, H-Y. & Mum, L-F 1996. Tuple Level Anaysis
for Identification of Interesting Rules. In Technical Report TRA5/95,
SoC, National University of Singapore, Singapore.
[7] Liu, B. & Hsu, W. & Chen, S. & Ma, Y. 2000. Analyzing the Subjective
Interestingness of Association Rules. IEEE Intelligent Systems.
[8] Padmanabhan, B. & Tuzhilin, A. 1997. Unexpectedness as a Measure of
Interestingness in Knowledge Discovery. Working paper # IS-97- Dept.
of Information Systems. Stern School of Business, NYU.
[9] Padmanabhan, B. & Tuzhilin, A. 1998. A Belief-Driven Method for
Discovering Unexpected Patterns. KDD-98.
[10] Padmanabhan, B. & Tuzhilin, A. 2000. Small is Beautiful: Discovering
the Minimal Set of Unexpected Patterns. KDD-2000.
[11] Ras, Z.W. & Tzacheva, A., In search for action rules of the lowest cost.
In "Monitoring, Security and Rescue Techniques in Multiagent Systems".
Advances in Soft Computing , Springer-Verlag, 2005, 261-272
[12] Ras, Z.W. & Wieczorkowska, A.. Action Rules : How to increase profit
of a company. In Principles of Data Mining and Knowledge Discovery,
(Eds. D. A. Zighed, J. Komorowski, J. Zytkow) Proceedings of
PKDD-00, Lyon, France, LNCS/LNAI, No. 1910, Springer-Verlag
2000, 587-592.
[13] Silberschatz, A. & Tuzihilin, A. 1995. On Subjective Measures of
Interestingness in Knowledge Discovery. In Proceedings of the 1st
International Conference on Knowledge Discovery and Data Mining.
[14] Silberschatz, A. & Tuzihilin, A.1996. What makes patterns Interesting
in Knowledge Discovery Systems. IEEE Trans. And Data Engineering.
Vol.5, no. 6.
[15] Suzuki, E. 1997. Autonomous Discovery of Reliable Exception Rules. In
Proceedings of the 3rd International Conference on Knowledge
Discovery and Data Mining. Newport Beach, CA, USA.
[16] Tsay, L.-S., & Ras, Z. W. (2004). Action Rules Discovery System DEAR,
Method and Experiments. In the Special Issue on Knowledge Discovery,
( Ed. Z. W. Ras), Journal of Experimental and Theoretical Artificial
Intelligence, Taylor & Francis.
[17] Tsay, L.-S., & Ras, Z. W. (2005). Action Rules Discovery System
DEAR2, Method and Experiments. In the Special Issue on Knowledge
Discovery, ( Ed. Z. W. Ras), Journal of Experimental and Theoretical
Artificial Intelligence, Taylor & Francis. Vol. 17, No.1-2, January-June
2005, 119-128.
[18] Wang, K., & Zhou, S., & Han. J. (1999). Profit Mining: From Patterns
to Actions. National Science and Engineering Research Council of
Canada.
[19] Yang, Q., & Yin, J. and Ling, X. C., & Chen, T. Postprocessing
Decision Trees to Extract Actionable Knowledge . In IEEE International
Conference on Data Mining (ICDM-03).
@article{"International Journal of Information, Control and Computer Sciences:52553", author = "Harleen Kaur", title = "Actionable Rules: Issues and New Directions", abstract = "Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the KDD process that aims at selecting and applying a particular data mining algorithm to extract an interesting and useful knowledge. It is highly expected that data mining methods will find interesting patterns according to some measures, from databases. It is of vital importance to define good measures of interestingness that would allow the system to discover only the useful patterns. Measures of interestingness are divided into objective and subjective measures. Objective measures are those that depend only on the structure of a pattern and which can be quantified by using statistical methods. While, subjective measures depend only on the subjectivity and understandability of the user who examine the patterns. These subjective measures are further divided into actionable, unexpected and novel. The key issues that faces data mining community is how to make actions on the basis of discovered knowledge. For a pattern to be actionable, the user subjectivity is captured by providing his/her background knowledge about domain. Here, we consider the actionability of the discovered knowledge as a measure of interestingness and raise important issues which need to be addressed to discover actionable knowledge.
", keywords = "Data Mining Community, Knowledge Discovery inDatabases (KDD), Interestingness, Subjective Measures,Actionability.", volume = "1", number = "5", pages = "1229-4", }
