In traditional data mining process, the definition of mining objects and analysis tasks are all decided artificially based on the analysts’ knowledge and experience. To achieve intelligent data analysis, a method called thinking theme discovery tech...
In traditional data mining process, the definition of mining objects and analysis tasks are all decided artificially based on the analysts’ knowledge and experience. To achieve intelligent data analysis, a method called thinking theme discovery technology is proposed to imitate humans’ thinking models. Since traditional thinking theme discovery algorithm is based on hierarchical clustering, the efficiency of which is far from acceptable with the increasing of data amounts. This paper improves the efficiency of the algorithm on density-based clustering method. With five complex network datasets and one commercial theme dataset, the experimental results show that both the effectiveness and efficiency of the algorithm are improved.