Knowledge discovery in databases: an overview
AI Magazine
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Parallel Algorithms for Discovery of Association Rules
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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Constraint Logic Programming using Eclipse
Constraint Logic Programming using Eclipse
Soft constraint based pattern mining
Data & Knowledge Engineering
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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The basic goal of data mining is to discover patterns occurring in the databases, such as associations, classification models, sequential patterns, and so on. In this paper we focus on the problem of frequent pattern discovery, which is the process of searching for patterns such as sets of features or items that appear in data frequently. Such frequent patterns can reveal associations, correlations, and many other interesting relationships hidden in a database. Most of frequent pattern mining systems in the market are too generic and become inefficient when set of patterns is large and the frequent patterns are very long. A new trend in data mining is a scalable method that uses constraints to guide the system in its search for interesting patterns. Our main research objective is the development of constraint-based mining methodology and this paper presents the preliminary results of our study and prototype development. We present the implementation of frequent pattern mining system based on declarative programming paradigm using logic programming and constraint logic programming. The comparative performance studies on speed and memory usage of logic versus constraint programming are also reported in the paper.