  Identifying high-dimensional data patterns without a priori knowledge is an
important task of data science. This paper proposes a simple and efficient
noparametric algorithm: Data Convert to Sequence Analysis, DCSA, which
dynamically explore each point in the feature space without repetition, and a
Directed Hamilton Path will be found. Based on the change point analysis
theory, The sequence corresponding to the path is cut into several fragments to
achieve clustering. The experiments on real-world datasets from different
fields with dimensions ranging from 4 to 20531 confirm that the method in this
work is robust and has visual interpretability in result analysis.
