How many clusters are best?—an experiment
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Least Biased Fuzzy Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Local Search in Conceptual Clustering
DS '01 Proceedings of the 4th International Conference on Discovery Science
Comparison of Three Objective Functions for Conceptual Clustering
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Data mining tasks and methods: Clustering: conceptual clustering
Handbook of data mining and knowledge discovery
| Hi-index | 0.14 |
A methodology for clustering data in which a distance metric or similarity function is not used is described. Instead, clusterings are optimized based on their intended function: the accurate prediction of properties of the data. The resulting clustering methodology is applicable, without further ad hoc assumptions or transformations of the data, (1) when features are heterogeneous (both discrete and continuous) and not combinable, (2) where some data points have missing feature values, and (3) where some features are irrelevant, i.e. have large variance but little correlation with other features. Further, it provides an integral measure of the quality of the resulting clustering. A clustering program, RIFFLE, has been implemented in line with this approach, and experiments with synthetic and real data show that the clustering is, in many respects, superior to traditional methods.