  As one type of efficient unsupervised learning methods, clustering algorithms
have been widely used in data mining and knowledge discovery with noticeable
advantages. However, clustering algorithms based on density peak have limited
clustering effect on data with varying density distribution (VDD), equilibrium
distribution (ED), and multiple domain-density maximums (MDDM), leading to the
problems of sparse cluster loss and cluster fragmentation. To address these
problems, we propose a Domain-Adaptive Density Clustering (DADC) algorithm,
which consists of three steps: domain-adaptive density measurement, cluster
center self-identification, and cluster self-ensemble. For data with VDD
features, clusters in sparse regions are often neglected by using uniform
density peak thresholds, which results in the loss of sparse clusters. We
define a domain-adaptive density measurement method based on K-Nearest
Neighbors (KNN) to adaptively detect the density peaks of different density
regions. We treat each data point and its KNN neighborhood as a subgroup to
better reflect its density distribution in a domain view. In addition, for data
with ED or MDDM features, a large number of density peaks with similar values
can be identified, which results in cluster fragmentation. We propose a cluster
center self-identification and cluster self-ensemble method to automatically
extract the initial cluster centers and merge the fragmented clusters.
Experimental results demonstrate that compared with other comparative
algorithms, the proposed DADC algorithm can obtain more reasonable clustering
results on data with VDD, ED and MDDM features. Benefitting from a few
parameter requirements and non-iterative nature, DADC achieves low
computational complexity and is suitable for large-scale data clustering.
