Abstract:

 Visual-based density clustering algorithm is insensitive to the initialized parameters, identify the data with any shape and can find the optimal cluster. One-pass clustering algorithm is efficient and fast. Based on their features we do research on a clustering algorithm which can process the data with mixing attributes. At first, the visual-based density clustering algorithm was improved slightly, which enabled it to process the data with categorical attributes. Then, the two-stage clustering algorithm was put forward. In the first stage, single pass clustering algorithm was used to group the data as an original partition. In the second stage, improved visual-based density clustering algorithm was used to merge the original partition so that the clusters are finally obtained. The experimental results of both the actual and synthetic datasets show that the presented clustering algorithm is effective and practicable.

Key words:  single pass clustering algorithm, visual theory clustering, arbitrary shape cluster