KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Inducing classification and regression trees in first order logic
Relational Data Mining
Discovery of relational association rules
Relational Data Mining
Distance based approaches to relational learning and clustering
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Inductive Learning in Deductive Databases
IEEE Transactions on Knowledge and Data Engineering
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A unified and flexible framework for comparing simple and complex patterns
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
On joining and caching stochastic streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Online clustering of parallel data streams
Data & Knowledge Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Memory-limited execution of windowed stream joins
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Top-down induction of first-order logical decision trees
Artificial Intelligence
Stream Clustering of Growing Objects
DS '09 Proceedings of the 12th International Conference on Discovery Science
Tree induction over perennial objects
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Regression on evolving multi-relational data streams
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
Precise anytime clustering of noisy sensor data with logarithmic complexity
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Classification rule mining for a stream of perennial objects
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Where are we going? predicting the evolution of individuals
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets
International Journal of Data Warehousing and Mining
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Many data mining applications analyze structured data that span across many tables and accumulate in time. Incremental mining methods have been devised to adapt patterns to new tuples. However, they have been designed for data in one table only. We propose a method for incremental clustering on multiple interrelated streams - a "multi-table stream ": its components are streams that reference each other, arrive at different speeds and have attributes of a priori unknown value ranges. Our approach encompasses solutions for the maintenance of cach-es and sliding windows over the individual streams, the propagation of foreign keys across streams, the transformation of all streams into a single-table stream, and an incremental clustering algorithm that operates over that stream. We evaluate our method on two real datasets and show that it approximates well the performance of an ideal method that possesses unlimited resources and knows the future.