In using a classified data set to test clustering algorithms, the data points in a class are considered as one cluster (or more than one) in space. In this paper we adopt this principle to build classification models through interactively clustering a training data set to construct a tree of clusters. The leaf clusters of the tree are selected as decision clusters to classify new data based on a distance function. We consider the feature weights in calculating the distances between a new object and the center of a decision cluster. The new algorithm, W-k-means, is used to automatically calculate the feature weights from the training data. The Fastmap technique is used to handle outliers in selecting decision clusters. This step increases the stability of the classifier. Experimental results on public domain data sets have shown that the models built using this clustering approach outperformed some popular classification algorithms.
['Liping Jing', 'Joshua Huang', 'Michael K. Ng', 'Hongqiang Rong']
A feature weighting approach to building classification models by interactive clustering