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

Discovering clusters from a dataset with different shapes, densities, andscales is a known challenging problem in data clustering. In this paper, wepropose the RElative COre MErge (RECOME) clustering algorithm. The core ofRECOME is a novel density measure, i.e., Relative $K$ nearest Neighbor KernelDensity (RNKD). RECOME identifies core objects with unit RNKD, and {partitions}non-core objects into atom clusters by successively following higher-densityneighbor relations toward core objects. Core objects and their correspondingatom clusters are then merged through $\alpha$-reachable paths on a KNN graph.We discover that the number of clusters computed by RECOME is a step functionof the $\alpha$ parameter with jump discontinuity on a small collection ofvalues. A fast jump discontinuity discovery (FJDD) method is proposed based ongraph theory. RECOME is evaluated on both synthetic datasets and real datasets.Experimental results indicate that RECOME is able to discover clusters withdifferent shapes, densities, and scales. It outperforms six baseline methods onboth synthetic datasets and real datasets. Moreover, FJDD is shown to beeffective to extract the jump discontinuity set of parameter $\alpha$ for alltested datasets, which can ease the task of data exploration and parametertuning.

RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density