Initialization of the Neighborhood EM Algorithm for Spatial Clustering

Authors:
Tianming Hu;Ji Ouyang;Chao Qu;Chuanren Liu
Affiliations:
Dongguan University of Technology, Dongguan;Dongguan University of Technology, Dongguan;Dongguan University of Technology, Dongguan;Beihang University, Beijing
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

Like other iterative refinement clustering algorithms, the Neighborhood Expectation-Maximization (NEM) algorithm is sensitive to the initial state of cluster separation. Therefore, the study of the initialization methods is of great importance for the success of finding a better sub-optimal solution in practice. However, existing initialization methods for mixture model based clustering using EM-style algorithms do not account for the unique properties of spatial data, such as spatial autocorrelation. To that end, this paper incorporates spatial information into the initialization and compares three representative initialization methods. Our experimental results on both synthetic and real-world datasets show that the NEM algorithm usually leads to a better clustering solution if it starts with initial states returned by the spatial augmented initialization method based on K-Means.