C4.5: programs for machine learning
C4.5: programs for machine learning
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing the Interestingness of Association Rules from the Temporal Dimension
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On Incorporating Subjective Interestingness Into the Mining Process
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining unexpected rules by pushing user dynamics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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Effective data mining techniques are crucial for analysing and extracting useful information from large datasets. The aim of this research is to investigate different techniques for detecting the variation in association rules over time. Analysing the rules instead of the large datasets allows one to have a better understanding of the overall trend of the entire database. In this paper, we propose a methodology to extract and categorise rules from a time driven database. We develop metrics to identify the level of variation for each rule and the entire rule set over a certain time period. The metrics exploit multi-resolution techniques to improve computational performance. In our experiment, we apply the proposed methods to different sets of simulated data. We demonstrate how simple patterns can be detected in more complex datasets. Additionally, we illustrate how these patterns can be rapidly identified using a graphical representation.