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
DBMiner: interactive mining of multiple-level knowledge in relational databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
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
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
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on 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
Interactivity Closes the GapLessons Learned in an Automotive Industry Application
Proceedings of the 2010 conference on Data Mining for Business Applications
A performance study of three disk-based structures for indexing and querying frequent itemsets
Proceedings of the VLDB Endowment
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In this paper, we report a deployed data mining application system for Motorola. Originally, its intended use was for identifying causes of cellular phone failures, but it has been found to be useful for many other engineering data sets as well. For this report, the case study is a dataset containing cellular phone call records. This data set is like any dataset used in classification applications, i.e., with a set of attributes which can be continuous or discrete, and a discrete class attribute. In our application, the classes are normally ended calls, calls which failed to setup, and calls which failed while in progress. However, the task is not to predict any failure, but to identify possible causes that resulted in failures. Then, engineering efforts may focus on improvements that can be made to the phones. In the course of the project, various classification techniques, e.g., decision trees, naïve Bayesian classification and SVM were tried. However, the results were unsatisfactory. After several demonstrations and interaction with domain experts, we finally designed and implemented an effective approach to perform the task. The final system is based on class association rules, general impressions and visualization. The system has been deployed and is in regular use at Motorola. In this paper, we first describe our experiences with some existing classification systems and discuss why they are not suitable for the task. We then present our techniques. As an illustration, we show several visualization screens in the case study, which reveal some important knowledge. Due to confidentiality, we will not give specifics but only present a general discussion about the results.