Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Study on Reliability in Graph Discovery
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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When conducting a clustering process, users are generally concerned whether the clustering result is reliable enough to reflect the actual clustering phenomenon. The number of clusters and initial cluster centers are two critical parameters that influence the reliability of clustering results highly. We propose a Clustering-Oriented Star Coordinate Translation (COSCT) method to help users determining the two parameters more confidently. Through COSCT all objects from a multi-dimensional space are adaptively translated to a 2D starcoordinate plane, so that the clustering parameterization can be easily conducted by observing the clustering phenomenon in the plane. To enhance the cluster-displaying quality of the star-coordinate plane, the feature weighting and coordinate arrangement procedures are developed. The effectiveness of the COSCT method is demonstrated using a set of experiments.