Using Gaussian Measures for Efficient Constraint Based Clustering
This work addresses the need for efficient and high-quality clustering in high-dimensional data analysis, though it appears incremental as it builds on existing clustering feature tree methods.
The paper tackles the problem of clustering high-dimensional data by introducing a novel iterative multiphase technique that uses a Gaussian density distribution constraint on a clustering feature tree to refine clusters and improve quality, with evaluation showing it overcomes drawbacks of conventional hierarchical methods.
In this paper we present a novel iterative multiphase clustering technique for efficiently clustering high dimensional data points. For this purpose we implement clustering feature (CF) tree on a real data set and a Gaussian density distribution constraint on the resultant CF tree. The post processing by the application of Gaussian density distribution function on the micro-clusters leads to refinement of the previously formed clusters thus improving their quality. This algorithm also succeeds in overcoming the inherent drawbacks of conventional hierarchical methods of clustering like inability to undo the change made to the dendogram of the data points. Moreover, the constraint measure applied in the algorithm makes this clustering technique suitable for need driven data analysis. We provide veracity of our claim by evaluating our algorithm with other similar clustering algorithms. Introduction