Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
A Bayesian approach for structural learning with hidden Markov models
Scientific Programming - Hidden Markov Models
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
ITERATE: a conceptual clustering algorithm for data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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When a partitional structure is derived from a data set using a data mining algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. To overcome this problem, the first clustering process proceeds to construct an initial partition. The partition is expected to imply the possible range in the number of final clusters. We apply center sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm, REIT, that achieves both efficiency and reliability. A number of experiments have been performed to show that the algorithm helps minimize the order bias effects.