An Attempt to Find Information for Multi-dimensional Data Sets

In this paper, we present our work on analyzing data sets that contain a large amount of data points. We study similarity search problems that find data points closest to a given query point. We also study cluster analysis that detects subgroups of data points from a data set that are similar to each other within the same subgroup. In this paper we design an algorithm to detect the clusters in subspaces that are readjusted continuously when the data set changes and new query requests come. The reconstructed clusters can help improve the performance of the future K nearest search process.