Knowledge discovery in databases: an overview
AI Magazine
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Exploiting succinct constraints using FP-trees
ACM SIGKDD Explorations Newsletter
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Mining of Constrained Frequent Patterns from Streams
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Methods for mining frequent items in data streams: an overview
Knowledge and Information Systems
The hows, whys, and whens of constraints in itemset and rule discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A new class of constraints for constrained frequent pattern mining
Proceedings of the 27th Annual ACM Symposium on Applied Computing
| Hi-index | 0.00 |
One important challenge in data mining is the ability to deal with complex, voluminous and dynamic data. Indeed, due to the great advances in technology, in many real world applications data appear in the form of continuous data streams, as opposed to traditional static datasets. Several techniques have been proposed to explore data streams, in particular for the discovery of frequent co-occurrences in data. However, one of the common criticisms pointed out to frequent pattern mining is the fact that it generates a huge number of patterns, independent of user expertise, making it very hard to analyze and use the results. These bottlenecks are even more evident when dealing with data streams, since new data are continuously and endlessly arriving, and many intermediate results must be kept in memory. The use of constraints to filter the results is the most common and used approach to focus the discovery on what is really interesting. In this sense, there is a need for the integration of data stream mining with constrained mining. In this work we describe a set of strategies for pushing constraints into data stream mining, through the use of a pattern tree structure that captures a summary of the current possible patterns. We also propose an algorithm that discovers patterns in data streams that satisfy any user defined constraint.