A Subspace Clustering Algorithm of High Dimension Data Based on Hybrid-Grid Partitioning
XU Chang-sen · Computer Technology and Development · 2010
A subspace clustering algorithm of high dimension data set based on hybrid-grid partitioning is proposed.The impact of attribute values range to the calculation is eliminated,filtering out redundant attributes is effective to enhance the clustering accuracy and reduce time complexity.The flexibility to choose a fixed or adaptive grid partition using the advantage of them to improve time complexity and the accuracy of clustering according to the data distribution.The algorithm has better scalability,too.A set of experiments on a synthetic dataset demonstrate the effectiveness and efficiency of the algorithms when clustering on high dimensional and large-scale data with the big range of the attribute value.