A cookbook for using the model-view controller user interface paradigm in Smalltalk-80
Journal of Object-Oriented Programming
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Coordinating agent activities in knowledge discovery processes
WACC '99 Proceedings of the international joint conference on Work activities coordination and collaboration
Online association rule mining
SIGMOD '99 Proceedings of the 1999 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
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Human-guided simple search: combining information visualization and heuristic search
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A human-computer cooperative system for effective high dimensional clustering
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
What's interesting about Cricket?: on thresholds and anticipation in discovered rules
ACM SIGKDD Explorations Newsletter
Knowledge refinement based on the discovery of unexpected patterns in data mining
Decision Support Systems - Special issue: Formal modeling and electronic commerce
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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
Speed-up Iterative Frequent Itemset Mining with Constraint Changes
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An iterative hypothesis-testing strategy for pattern discovery
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualising hierarchical associations
Knowledge and Information Systems
MCFPTree: An FP-tree-based algorithm for multi-constraint patterns discovery
International Journal of Business Intelligence and Data Mining
A new parallel association rule mining algorithm on distributed shared memory system
International Journal of Business Intelligence and Data Mining
Exploratory mining over organisational communications data
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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The quality of data mining results is largely dependent on the ability to accommodate context and user requirements within the mining process. This is done effectively within the pre-processing and presentation stages, however the analysis (or mining) stage remains relatively autonomous and opaque with user input commonly limited to parameter setting. There is, at present, no direct manipulation of the analysis stage which results in the analysis of the domain space being statically constrained. This reduces the quality of results and increases the time needed for analysis. This paper presents a guided association mining environment, GAM, that enhances user-computer synergy by incorporating the user at fine level of granularity within the analysis stage. GAM extends the current state of the art and is based upon a generic guided knowledge discovery environment.