Forward-Stagewise Clustering: An Algorithm for Convex Clustering - Zhang, Mimi (My University, 2019)
This paper proposes an exceptionally simple algorithm, called forward-stagewise clustering, for convex clustering. Convex clustering has drawn recent attention since it nicely addresses the instability issue of traditional non-convex clustering methods. While existing algorithms can precisely solve convex clustering problems, they are sophisticated and produce (agglomerative) clustering paths that contain splits. This motivates us to propose an algorithm that only produces no-split clustering paths.The approach undertaken here follows the line of research initiated in the area of regression. Specifically, we apply the forward-stagewise technique to clustering problems and prove that the algorithm can only produce no-split clustering paths. We then modify the forward-stagewise clustering algorithm to deal with noise and outliers. We further suggest rules of thumb for the algorithm to be applicable to cases where clusters are non-convex. The performance of the proposed algorithm is evaluated through simulations and a real data application.
PUBLISHED
Keywords: Algorithms; Forward-stagewise clustering; Convex clustering; Fusion penalty; Generalized lasso; Hierarchical clustering; K-nearest neighbor