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 techniques are widely used in Web Usage Mining to capture similarinterests and trends among users accessing a Web site. For this purpose, webaccess logs generated at a particular web site are preprocessed to discover theuser navigational sessions. Clustering techniques are then applied to group theuser session data into user session clusters, where intercluster similaritiesare minimized while the intra cluster similarities are maximized. Since theapplication of different clustering algorithms generally results in differentsets of cluster formation, it is important to evaluate the performance of thesemethods in terms of accuracy and validity of the clusters, and also the timerequired to generate them, using appropriate performance measures. This paperdescribes various validity and accuracy measures including Dunn's Index, DaviesBouldin Index, C Index, Rand Index, Jaccard Index, Silhouette Index, FowlkesMallows and Sum of the Squared Error (SSE). We conducted the performanceevaluation of the following clustering techniques: k-Means, k-Medoids, Leader,Single Link Agglomerative Hierarchical and DBSCAN. These techniques areimplemented and tested against the Web user navigational data. Finally theirperformance results are presented and compared.

Quantitative Evaluation of Performance and Validity Indices for Clustering the Web Navigational Sessions