English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The clustering analysis is an important unsupervised learning algorithm in data mining, which has a wide range of applications in the field of pattern recognition, image processing, and so on. The existing clustering algorithms generally need one or more pre-setting parameters, these parameters lack the effective acquisition method and are often determined empirically, which has a great influence on the performance and robustness of the clustering algorithm. In this paper, we propose a new parameter-free clustering algorithm. We transform the unclassified data set into a weighted complete graph and use Laplacian centrality to characterize the local importance of data points. Then the density-based spatial clustering of application with noise (DBSCAN) framework is used to find the number of clusters and complete the clustering, in which the optimal parameters of DBSCAN are automatically extracted from the original data by potential entropy method. The feature of the new clustering algorithm is that the parameters needed by the algorithm can be extracted from the original data and the correct number of clusters is automatically found, which achieve true parameter-free clustering. We compare the algorithm with seven well-known clustering algorithms in 10 data sets. The simulation results show that the new algorithm has good clustering effect.

Laplacian Centrality Peaks Clustering Based on Potential Entropy