C4.5: programs for machine learning
C4.5: programs for machine learning
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
The nature of statistical learning theory
The nature of statistical learning theory
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing association rules with interactive mosaic plots
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
RuleViz: a model for visualizing knowledge discovery process
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Knowledge refinement based on the discovery of unexpected patterns in data mining
Decision Support Systems - Special issue: Formal modeling and electronic commerce
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Querying multiple sets of discovered rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining unexpected rules by pushing user dynamics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
V-Miner: using enhanced parallel coordinates to mine product design and test data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Visual Data Mining Framework for Convenient Identification of Useful Knowledge
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Managing large collections of data mining models
Communications of the ACM - Alternate reality gaming
Generating design knowledge though data mining
Journal of Computing Sciences in Colleges
Granule Oriented Data Warehouse Model
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Evaluating statistical tests on OLAP cubes to compare degree of disease
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Cube based summaries of large association rule sets
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A novel evolutionary method to search interesting association rules by keywords
Expert Systems with Applications: An International Journal
Searching interesting association rules based on evolutionary computation
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Adaptive Study Design Through Semantic Association Rule Analysis
International Journal of Software Science and Computational Intelligence
A performance study of three disk-based structures for indexing and querying frequent itemsets
Proceedings of the VLDB Endowment
A hybrid heuristic approach for attribute-oriented mining
Decision Support Systems
Discovering frequent pattern pairs
Intelligent Data Analysis
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The problem of interestingness of discovered rules has been investigated by many researchers. The issue is that data mining algorithms often generate too many rules, which make it very hard for the user to find the interesting ones. Over the years many techniques have been proposed. However, few have made it to real-life applications. Since August 2004, we have been working on a major application for Motorola. The objective is to find causes of cellular phone call failures from a large amount of usage log data. Class association rules have been shown to be suitable for this type of diagnostic data mining application. We were also able to put several existing interestingness methods to the test, which revealed some major shortcomings. One of the main problems is that most existing methods treat rules individually. However, we discovered that users seldom regard a single rule to be interesting by itself. A rule is only interesting in the context of some other rules. Furthermore, in many cases, each individual rule may not be interesting, but a group of them together can represent an important piece of knowledge. This led us to discover a deficiency of the current rule mining paradigm. Using non-zero minimum support and non-zero minimum confidence eliminates a large amount of context information, which makes rule analysis difficult. This paper proposes a novel approach to deal with all of these issues, which casts rule analysis as OLAP operations and general impression mining. This approach enables the user to explore the knowledge space to find useful knowledge easily and systematically. It also provides a natural framework for visualization. As an evidence of its effectiveness, our system, called Opportunity Map, based on these ideas has been deployed, and it is in daily use in Motorola for finding actionable knowledge from its engineering and other types of data sets.