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Clustering with Soft and Group Constraints
[chapter]
2004
Lecture Notes in Computer Science
Several clustering algorithms equipped with pairwise hard constraints between data points are known to improve the accuracy of clustering solutions. We develop a new clustering algorithm that extends mixture clustering in the presence of (i) soft constraints, and (ii) grouplevel constraints. Soft constraints can reflect the uncertainty associated with a priori knowledge about pairs of points that should or should not belong to the same cluster, while group-level constraints can capture larger
doi:10.1007/978-3-540-27868-9_72
fatcat:g6acmcfsbnaspdtjtmkzglzwpu