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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Clustering analysis is one of the critical tasks in machine learning.Traditionally, clustering has been an independent task, separate from outlierdetection. Due to the fact that the performance of clustering can besignificantly eroded by outliers, a small number of algorithms try toincorporate outlier detection in the process of clustering. However, most ofthose algorithms are based on unsupervised partition-based algorithms such ask-means. Given the nature of those algorithms, they often fail to deal withclusters of complex, non-convex shapes. To tackle this challenge, we haveproposed SSDBCODI, a semi-supervised density-based algorithm. SSDBCODI combinesthe advantage of density-based algorithms, which are capable of dealing withclusters of complex shapes, with the semi-supervised element, which offersflexibility to adjust the clustering results based on a few user labels. Wealso merge an outlier detection component with the clustering process.Potential outliers are detected based on three scores generated during theprocess: (1) reachability-score, which measures how density-reachable a pointis to a labeled normal object, (2) local-density-score, which measures theneighboring density of data objects, and (3) similarity-score, which measuresthe closeness of a point to its nearest labeled outliers. Then in the followingstep, instance weights are generated for each data instance based on thosethree scores before being used to train a classifier for further clustering andoutlier detection. To enhance the understanding of the proposed algorithm, forour evaluation, we have run our proposed algorithm against some of thestate-of-art approaches on multiple datasets and separately listed the resultsof outlier detection apart from clustering. Our results indicate that ouralgorithm can achieve superior results with a small percentage of labels.

SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated