Clustering by growing incremental self-organizing neural network

• A topological graph visualizes both data partitions and details of each cluster.
• The presented approach aims to learn data distribution of each cluster.
• The number of clusters is not a prerequisite for clustering tasks.
• The proposed method is able to detect arbitrary-shaped clusters.

This paper presents a new clustering algorithm that detects clusters by learning data distribution of each cluster. Different from most existing clustering techniques, the proposed method is able to generate a dynamic two-dimensional topological graph which is used to explore both partitional information and detailed data relationship in each cluster. In addition, the proposed method is also able to work incrementally and detect arbitrary-shaped clusters without requiring the number of clusters as a prerequisite. The experimental data sets including five artificial data sets with various data distributions and an original hand-gesture data set are used to evaluate the proposed method. The comparable experimental results demonstrate the superior performance of the proposed algorithm in learning robustness, efficiency, working with outliers, and visualizing data relationships.