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
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
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
Design of hierarchical fuzzy model for classification problem using GAs
Computers and Industrial Engineering
A simultaneous learning framework for clustering and classification
Pattern Recognition
Rule induction based on an incremental rough set
Expert Systems with Applications: An International Journal
Design of hierarchical fuzzy model for classification problem using GAs
Computers and Industrial Engineering
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Simultaneous clustering and classification over cluster structure representation
Pattern Recognition
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In this paper a novel data mining algorithm, Clustering and Classification Algorithm-Supervised (CCA-S), is introduced. CCA-S enables the scalable, incremental learning of a non-hierarchical cluster structure from training data. This cluster structure serves as a function to map the attribute values of new data to the target class of these data, that is, classify new data. CCA-S utilizes both the distance and the target class of training data points to derive the cluster structure. In this paper, we first present problems with many existing data mining algorithms for classification problems, such as decision trees, artificial neural networks, in scalable and incremental learning. We then describe CCA-S and discuss its advantages in scalable, incremental learning. The testing results of applying CCA-S to several common data sets for classification problems are presented. The testing results show that the classification performance of CCA-S is comparable to the other data mining algorithms such as decision trees, artificial neural networks and discriminant analysis.