Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Information Sciences: an International Journal
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Clustering quality based feature selection method
Machine Graphics & Vision International Journal
A clustering method to identify representative financial ratios
Information Sciences: an International Journal
Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan
Expert Systems with Applications: An International Journal
Innovation in the cluster validating techniques
Fuzzy Optimization and Decision Making
Expert Systems with Applications: An International Journal
Discovering conjecturable rules through tree-based clustering analysis
Expert Systems with Applications: An International Journal
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Artificial Intelligence in Medicine
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A clustering method based on fuzzy equivalence relation for customer relationship management
Expert Systems with Applications: An International Journal
A clustering system for data sequence partitioning
Expert Systems with Applications: An International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Clustering methods can be viewed as unsupervised learning from a given dataset. Even without domain knowledge or labels such as the names of diseases given by medical experts, these methods generate partition of datasets. In some cases, these new generated classes lead to discovery of a new disease or new concept. This paper discusses how clustering methods work on a practical medical data set. For comparison, the following four clustering methods were selected and evaluated on a dataset on meningoencephalitis: single- and complete-linkage agglomerative hierarchical clustering, Ward's method and rough clustering. For comparison, a single similarity measure, a linear combination of the Mahalanobis distance between numerical attributes and the Hamming distance between nominal attributes was given to each clustering method. Usefulness of the clustering methods was evaluated from the following viewpoints: (1) the quality of generated clusters, (2) correspondence between the attributes used to generate the high-quality clusters and clinical knowledge. The experimental results showed that the best clusters were obtained using Ward's method where the clinically reasonable attributes were selected, which also suggested that this similarity measure would be applicable to the medical data sets.