Algorithms for clustering data
Algorithms for clustering data
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Data clustering for very large datasets plus applications
Data clustering for very large datasets plus applications
Machine Learning
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A subspace decision cluster classifier for text classification
Expert Systems with Applications: An International Journal
An ensemble of decision cluster crotches for classification of high dimensional data
Knowledge-Based Systems
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In this paper a novel data mining technique - Clustering and Classification Algorithm-Supervised (CCA-S) is introduced. CCA-S supports incremental learning and non-hierarchical clustering, and is scalable for processing large data sets. CCA-S incorporates the class information in making clustering decisions, and uses the resulting clusters to classify new data records. We apply and test CCA-S on several common data sets for classification problems. The testing results show that the classification performance of CCAS is comparable to the other classification algorithms such as decision trees, artificial neural networks and discriminant analysis.