Much different parallel construction density tree clustering (PCDTC) algorithm based on data partitioning

Aim. Like Ref.7 by Xia et al, we deal with the subject stated in the title; unlike Ref.7, our method is much different and, we believe, more efficient. In the full paper, we explain our algorithm and its performance in some detail; in this abstract, we just add some pertinent remarks to listing the three topics of explanation. The first topic is: the essentials of our algorithm. The second topic is: PCDTC algorithm. In this topic, we use our algorithm to partition high-dimensional data into orthogonal subspaces as shown in Fig.2. The third topic is: the analysis of the performance of PCDTC algorithm. The clustering results and their comparison with two other traditional methods, given in Tables 1 and 2 in the full paper, show preliminarily that our algorithm not only provides various types of effective clustering results but also enhances the clustering efficiency with adequate clustering precision ensured.