1. A computer-implemented method for in-database clustering in a database management method, comprising the steps of: receiving a first data table and a second data table, each data table including a plurality of data records; building an enhanced K-means clustering model using the first data table, comprising: initializing centroids of the clusters of the clustering model, finding, for each data record, a cluster closest to the data record, updating centroids of the clusters and histograms based on new record assignments, extracting at least one rule describing the updated clusters including defining a hyperbox from the histogram, which includes defining the hyperbox around the mean of a cluster using the histogram, for numeric attributes and including in the hyperbox attribute values with largest counts, for categorical attributes, generating at least one probabilistic model based on the histograms; and applying the enhanced K-means clustering model to the second data table to generate apply-output data.