Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Efficient reduction of the number of associations rules using fuzzy clustering on the data
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Data Mining: Concepts, Models, Methods, and Algorithms
Data Mining: Concepts, Models, Methods, and Algorithms
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Nowadays, with the evolution of the data in data processing and storage of great volumes of these diversified data, the software of Data Mining became without context a necessity for the majority of the users of the Information Systems. Unfortunately, currently marketed software are very limited and don't meet all user needs. This software supports only some classification algorithms and some Knowledge Discovery in Databases (KDD) algorithms that generate a big number of rules which are not understandable by the end user. Moreover, these approaches are applicable only for restricted data type. In this paper, we propose new software of classification and KDD, called Cluster-KDD, which supports a larger set of data type and classification algorithm and offers KDD algorithms that generate comprehensible and exploitable rules by the user.