Instance-Based Learning Algorithms
Machine Learning
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 SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 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
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
When is nearest neighbors indexable?
ICDT'05 Proceedings of the 10th international conference on Database Theory
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Most methods proposed so far for classification of high-dimensional data are memory-based and obtain a model of the data classes through training before actually performing any classification. As a result, these methods are ineffective on (a) very large datasets stored in databases or data warehouses, (b) data whose partitioning into classes cannot be captured by global models and is sensitive to local characteristics, and (c) data that arrives continuously to the system with pre-classified and unclassified instances mutually interleaved and whose successful classification is sensitive to using the most complete and/or most up-to-date information. In this paper, we propose LOCUS, a scalable model-free classifier that overcomes these problems. LOCUS is based on ideas from pattern recognition and is shown to converge to the optimal Bayes classifier as the size of the datasets involved increases. Moreover, LOCUS is data-scalable and can be implemented using standard SQL over arbitrary database tables. To the best of our knowledge, LOCUS is the first classifier that combines all the characteristics above. We demonstrate the effectiveness of LOCUS through experiments over both real-world and synthetic datasets, comparing it against memory-based decision trees. The results indicate an overall superiority of LOCUS over decision trees on both classification accuracy and data sizes that it can handle.