  Nowadays, data are generated massively and rapidly from scientific fields as
bioinformatics, neuroscience and astronomy to business and engineering fields.
Cluster analysis, as one of the major data analysis tools, is therefore more
significant than ever. We propose in this work an effective Semi-supervised
Divisive Clustering algorithm (SDC). Data points are first organized by a
minimal spanning tree. Next, this tree structure is transitioned to the in-tree
structure, and then divided into sub-trees under the supervision of the labeled
data, and in the end, all points in the sub-trees are directly associated with
specific cluster centers. SDC is fully automatic, non-iterative, involving no
free parameter, insensitive to noise, able to detect irregularly shaped cluster
structures, applicable to the data sets of high dimensionality and different
attributes. The power of SDC is demonstrated on several datasets.
