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
The nature of statistical learning theory
The nature of statistical learning theory
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Review: a quarter-century of the knowledge engineering review: Introduction to the special issue
The Knowledge Engineering Review
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Data mining has become a well-established discipline within the domain of artificial intelligence (AI) and knowledge engineering (KE). It has its roots in machine learning and statistics, but encompasses other areas of computer science. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale data mining to be conducted. Unlike other innovations in AI and KE, data mining can be argued to be an application rather then a technology and thus can be expected to remain topical for the foreseeable future. This paper presents a brief review of the history of data mining, up to the present day, and some insights into future directions.