Randomizing Outputs to Increase Prediction Accuracy
Machine Learning
Clustering Algorithms
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Clustering: A neural network approach
Neural Networks
Performance prediction methodology based on pattern recognition
Signal Processing
Information Sciences: an International Journal
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
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k-means is traditionally viewed as an algorithm for the unsupervised clustering of a heterogeneous population into a number of more homogeneous groups of objects. However, it is not necessarily guaranteed to group the same types (classes) of objects together. In such cases, some supervision is needed to partition objects which have the same label into one cluster. This paper demonstrates how the popular k-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The output field itself cannot be used in the clustering but it is used in developing a suitable metric defined on other fields. The proposed algorithm combines Simulated Annealing with the modified k-means algorithm. We apply the proposed algorithm to real data sets, and compare the output of the resultant classifier to that of C4.5.