C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Medical Knowledge Discovery on the Meningoencephalitis Diagnosis Studied by the Cascade Model
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Rule Induction in Cascade Model Based on Sum of Squares Decomposition
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Efficient Detection of Local Interactions in the Cascade Model
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A correlation-based approach to attribute selection in chemical graph mining
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Spiral mining using attributes from 3d molecular structures
AM'03 Proceedings of the Second international conference on Active Mining
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Association rules have the potential to express all kinds of valuable information, but a user often does not know what to do when he or she encounters numerous, unorganized rules. This paper introduces a new concept, the datascape survey. This provides an overview of data, and a way to go into details when necessary. We cannot invoke active user reactions to mining results, unless a user can view the datascape. The aim of this paper is to develop a set of rules that guides the datascape survey. The cascade model was developed from association rule mining, and it has several advantages that allow it to lay the foundation for a better expression of rules. That is, a rule denotes local correlations explicitly, and the strength of a rule is given by the numerical value of the BSS (between-groups sum of squares). This paper gives a brief overview of the cascade model, and proposes a new method of organizing rules. The method arranges rules into principal rules and associated relatives, using the relevance among supporting instances of the rules. Application to a real medical dataset is also discussed.