We investigate the role of the initialization for the stability of the қ-means clustering algorithm. As opposed to other papers, we consider the actual қ-means algorithm (also known as Lloyd algorithm). In particular we leverage on the property that this algorithm can get stuck in local optima of the қ-means objective function. We are interested in the actual clustering, not only in the costs of the solution. We analyze when different initializations lead to the same local optimum, and when they...
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph clustering algorithm to find a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) influences the outcome of the final clustering result. To this end we study the convergence of cluster quality measures...
We investigate the role of the initialization for the stability of the қ-means clustering
algorithm. As opposed to other papers, we consider the actual қ-means algorithm (also known
as Lloyd algorithm). In particular we leverage on the property that this algorithm can get
stuck in local optima of the қ-means objective function. We are interested in the actual
clustering, not only in the costs of the solution. We analyze when different
initializations...
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