A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

Abstract

Clustering aims to differentiate objects from different groups (clusters) by similarities or distances between pairs of objects. Numerous clustering algorithms have been proposed to investigate what factors constitute a cluster and how to efficiently find them. The clustering by fast search and find of density peak algorithm is proposed to intuitively determine cluster centers and assign points to corresponding partitions for complex datasets. This method incorporates simple structure due to the noniterative logic and less few parameters; however, the guidelines for parameter selection and center determination are not explicit. To tackle these problems, we propose an improved hierarchical clustering method HCDP aiming to represent the complex structure of the dataset. A k-nearest neighbor strategy is integrated to compute the local density of each point, avoiding to select the nonnecessary global parameter d c and enables cluster smoothing and condensing. In addition, a new clustering evaluation approach is also introduced to extract a "flat" and "optimal" partition solution from the structure by adaptively computing the clustering stability. The proposed approach is conducted on some applications with complex datasets, where the results demonstrate that the novel method outperforms its counterparts to a large extent.

Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

“…For example, [16] applied the concept of heat diffusion and [17] employed the potential entropy of the data field to assist in setting the radius . Also, many studies suggested using k nearest neighbors to define density, instead of using the radius [18][19][20][21]. Furthermore, [22] suggested calculating two kinds of densities, one based on k nearest neighbors and one based on local spatial position deviation, to handle datasets with mixed density clusters.…”

“…To resolve this problem, [23] proposed a comparative technique to choose the density peaks, [24] estimated density dips between points to determine the number of clusters, and [25] applied data detection to determine density peaks automatically. In [21], the optimal number of clusters was extracted from the results of hierarchical clustering. Furthermore, it may be more suitable for some datasets to locate a cluster by more than one density peak [26,27].…”

“…A density peak clustering (DPC) algorithm is a density-based clustering method proposed by Rodriguez and Laio [15] in 2014. Since its inception, DPC has received much attention in the research community [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Similar to other density-based methods, DPC calculates the density of each data point in the dataset.…”

Organizing Project Actors for Collective Decision‐Making about Interdependent Risks

“…They can be more or less supervised, notably on the number of clusters k, which is required for instance in the k-means method [53][54][55][56]. Cluster density has been developed to partition the initial graph into smaller ones, depending on local density values of each cluster [57][58][59][60][61]. Cut size-based measures allow the quantification of the dependence of a subgraph to the rest of the graph [62,63], with several indices, like for instance Dunn index [64], Davies-Boulding index [65], Xie and Beni's validity index [66], and Bezdek's partition coefficient and partition entropy [67,68].…”

New internal clustering validation measure for contiguous arbitrary‐shape clusters

“…Clustering algorithms group objects that are similar to each other in clusters. 1 Different clustering algorithms have been developed and applied to different data sets of very varied fields (medical databases, images, etc. ).…”