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 is one of the main tasks in exploratory data analysis anddescriptive statistics where the main objective is partitioning observations ingroups. Clustering has a broad range of application in varied domains likeclimate, business, information retrieval, biology, psychology, to name a few. Avariety of methods and algorithms have been developed for clustering tasks inthe last few decades. We observe that most of these algorithms define a clusterin terms of value of the attributes, density, distance etc. However thesedefinitions fail to attach a clear meaning/semantics to the generated clusters.We argue that clusters having understandable and distinct semantics defined interms of quartiles/halves are more appealing to business analysts than theclusters defined by data boundaries or prototypes. On the samepremise, wepropose our new algorithm named as quartile clustering technique. Through aseries of experiments we establish efficacy of this algorithm. We demonstratethat the quartile clustering technique adds clear meaning to each of theclusters compared to K-means. We use DB Index to measure goodness of theclusters and show our method is comparable to EM (Expectation Maximization),PAM (Partition around Medoid) and K Means. We have explored its capability indetecting outlier and the benefit of added semantics. We discuss some of thelimitations in its present form and also provide a rough direction inaddressing the issue of merging the generated clusters.

Quartile Clustering: A quartile based technique for Generating Meaningful Clusters