Attributes of an object contain its fundamental properties. Attribute data is the main source of clustering information. Although relationship data is an extrinsic property of objects and is at least as important as attribute data, most clustering methods process only one type of characteristic data. However, analyzing attribute and relationship data together in applications such as market segmentation, social network segmentation, and image segmentation can lead to better and more meaningful clustering. In this study, we describe a new algorithm that combines attribute and relationship data for joint clustering analysis. An experimental evaluation demonstrates the usefulness and accuracy of the proposed algorithm when applied to image segmentation.
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