Goodness-of-fit techniques
Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Borders: An Efficient Algorithm for Association Generation in Dynamic Databases
Journal of Intelligent Information Systems
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Incremental Induction of Decision Trees
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Optimized Association Rules with Categorical and Numeric Attributes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
DEMON: Mining and Monitoring Evolving Data
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
On growing better decision trees from data
On growing better decision trees from data
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
WAM-Miner: in the search of web access motifs from historical web log data
Proceedings of the 14th ACM international conference on Information and knowledge management
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Data mining algorithms have been the focus of much recent research. The initial spurt of research on data mining algorithms typically considered static datasets. In practice, the input data to a data mining process resides in a large data warehouse whose data is kept up-to-date through periodic or occasional insertion and deletion of sets of tuples. Consequently, several issues that arise in a dynamically evolving database have recently begun to receive widespread attention. In this article, we survey research on two important issues: (1) exploiting the systematic data evolution for efficiently maintaining data mining models, and (2) monitoring changes in data characteristics. We classify research addressing these two problems based on a few distinguishing characteristics, and then briefly discuss all techniques captured by this classification.