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/

Zendy Plus

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

Zendy Tools

Zendy Open

K-medoids clustering is a popular variant of k-means clustering and widelyused in pattern recognition and machine learning. A main drawback of k-medoidsclustering is that an improper initialization can cause it to get trapped inlocal optima. An improved k-medoids clustering algorithm, called INCKMalgorithm, which is the first to apply incremental initialization to k-medoidsclustering, was recently proposed to overcome this drawback. The INCKMalgorithm requires the construction of a subset of candidate medoids determinedby one hyperparameter for initialization, and meanwhile, it always fails whendealing with imbalanced datasets with an incorrect hyperparameter selection. Inthis paper, we propose a novel k-medoids clustering algorithm, calledincremental k-means++ (INCKPP) algorithm, which initializes with a novelincremental manner, attempting to optimally add one new cluster center at eachstage through a nonparametric and stochastic k-means++ initialization. TheINCKPP algorithm overcomes the difficulty of hyperparameter selection in theINCKM algorithm, improves the clustering performance, and can deal withimbalanced datasets well. However, the INCKPP algorithm is not computationallyefficient enough. To deal with this, we further propose an improved INCKPPalgorithm, called INCKPPsample algorithm, which improves the clusteringefficiency while maintaining the clustering performance of the INCKPPalgorithm. Extensive results from experiments on both synthetic and real-worlddatasets, including imbalanced datasets, illustrate that the proposedalgorithms outperforms than the other compared algorithms.

Careful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization