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This is partly because of differences in the way the algorithms select their first seed or centroid either randomly, sequentially or some other principles influences which tend to influence the final result outcome. Popular external cluster quality validation and comparison models require the computation of varying clustering indexes such as Rand, Jaccard, Fowlkes and Mallows, Morey and Agresti Adjusted Rand Index (ARI<jats:sub>MA<\/jats:sub>) and Hubert and Arabie Adjusted Rand Index (ARI<jats:sub>HA<\/jats:sub>). In literature, Hubert and Arabie Adjusted Rand Index (ARI<jats:sub>HA<\/jats:sub>) has been adjudged as a good measure of cluster validity. Based on ARI<jats:sub>HA<\/jats:sub> as a popular clustering quality index, we developed <jats:italic>OsamorSoft<\/jats:italic> which constitutes <jats:italic>DNA_Omatrix<\/jats:italic> and <jats:italic>OsamorSpreadSheet<\/jats:italic> as a tool for cluster quality validation in high throughput analysis. The proposed method will help to bridge the yawning gap created by lesser number of friendly tools available to externally evaluate the ever-increasing number of clustering algorithms. Our implementation was tested alongside with clusters created with four k-means algorithms using malaria microarray data. Furthermore, our results evolved a compact 4-stage <jats:italic>OsamorSpreadSheet<\/jats:italic> statistics that our easy-to-use GUI java and spreadsheet-based tool of <jats:italic>OsamorSoft<\/jats:italic> uses for cluster quality comparison. It is recommended that a framework be evolved to facilitate the simplified integration and automation of several other cluster validity indexes for comparative analysis of big data problems.<\/jats:p>","DOI":"10.1186\/s40537-020-00325-6","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T08:08:50Z","timestamp":1594282130000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["OsamorSoft: clustering index for comparison and quality validation in high throughput dataset"],"prefix":"10.1186","volume":"7","author":[{"given":"Ifeoma Patricia","family":"Osamor","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-0967","authenticated-orcid":false,"given":"Victor Chukwudi","family":"Osamor","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"key":"325_CR1","unstructured":"MacQueen J. 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