Conceptual clustering and its relation to numerical taxonomy
Artificial intelligence and statistics
Models of incremental concept formation
Artificial Intelligence
Ordering effects in clustering
ML92 Proceedings of the ninth international workshop on Machine learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Self-organizing maps as substitutes for k-means clustering
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
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It is widely reported in the literature that incremental clustering systems suffer from instance ordering effects and that under some orderings, extremely poor clusterings may be obtained. In this paper we present a new general strategy aimed to mitigate these effects, the Not-Yet strategy which has a general and open formulation and it is not coupled to any particular system. Unlike other proposals, this strategy maintains the incremental nature of learning process. In addition, we propose a classification of strategies to avoid ordering effects which clarifies the benefits and disadvantages we can expect from the proposal made in the paper as well from existing ones. A particular implementation of the Not-Yet strategy is used to conduct several experiments. Results suggest that the strategy improves the clustering quality. We also show that, when combined with other local strategies, the Not-Yet strategy allows the clustering system to get high quality clusterings.