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
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Peculiarity Oriented Multi-database Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On Modeling Data Mining with Granular Computing
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Granular computing using information tables
Data mining, rough sets and granular computing
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGMOD Record
Research issues in data stream association rule mining
ACM SIGMOD Record
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
WSFI-Mine: Mining Frequent Patterns in Data Streams
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A Semantic Model for Matchmaking of Web Services Based on Description Logics
Fundamenta Informaticae
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
Mining negative generalized knowledge from relational databases
Knowledge-Based Systems
An extended predictive model markup language for data mining
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Unsupervised image retrieval framework based on rule base system
Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
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Data take the form of continuous data streams rather than traditional stored databases in a growing number of applications, including network traffic monitoring, network intrusion detection, sensor networks, fraudulent transaction detection, financial monitoring, etc. People are interested in the potential rules in data streams such as association rules and decision rules. Compared with much work on developing algorithms of data streams mining, there is little attention paid on the modeling data mining and data streams mining. Considering the problem of conceptual modeling data streams mining, we put forward a data streams oriented decision logic language as a granular computing formal approach and a rules extracting model based on granular computing. In this model, we propose the notion of granular drifting, which accurately interpret the concept drifting problem in data streams. This model is helpful to understand the nature of data streams mining. Based on this model, new algorithms and techniques of data streams mining could be developed.