Algorithms for clustering data
Algorithms for clustering data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Building a Decision Cluster Forest Model to Classify High Dimensional Data with Multi-classes
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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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This paper presents the decision clusters classifier (DCC) for database mining. A DCC model consists of a small set of decision clusters extracted from a tree of clusters generated by a clustering algorithm from the training data set. A decision cluster is associated to one of the classes in the data set and used to determine the class of new objects. A DCC model classifies new objects by deciding which decision clusters these objects belong to. In making classification decisions, DCC is similar to the k-nearest neighbor classification scheme but its model building process is different. In this paper, we describe an interactive approach to building DCC models by stepwise clustering the training data set and validating the clusters using data visualization techniques. Our initial results on some public benchmarking data sets have shown that DCC models outperform the some existing popular classification methods.