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 widely used data mining techniques for medicaldiagnosis. Clustering can be considered as the most important unsupervisedlearning technique. Most of the clustering methods group data based on distanceand few methods cluster data based on similarity. The clustering algorithmsclassify gene expression data into clusters and the functionally related genesare grouped together in an efficient manner. The groupings are constructed suchthat the degree of relationship is strong among members of the same cluster andweak among members of different clusters. In this work, we focus on asimilarity relationship among genes with similar expression patterns so that aconsequential and simple analytical decision can be made from the proposedFuzzy Soft Rough K-Means algorithm. The algorithm is developed based on FuzzySoft sets and Rough sets. Comparative analysis of the proposed work is madewith bench mark algorithms like K-Means and Rough K-Means and efficiency of theproposed algorithm is illustrated in this work by using various clustervalidity measures such as DB index and Xie-Beni index.

Fuzzy soft rough K-Means clustering approach for gene expression data