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In this paper, unsupervised machine learning method like as k-means, hierarchical, and fuzzy c-means algorithm are shown to prove excellent classification performance and have been successfully applied in data analysis of H1N1 infected and non-infected datasets. At first, k-means clustering algorithm are applied on H1N1 infected dataset Influenza A/447/08 at Oklahoma, Influenza A/1138/08 at Oklahoma, and Influenza A/447/08 at Oklahoma and on non-infected dataset Influenza A/California/04/2009-4C that are shown in Figures 2.2 to 2.5. Same process will be repeated for hierarchical clustering algorithms that are shown in Figures 2.6 to 2.8. Fuzzy c-means has applied on above-mentioned datasets that are shown in Figures 2.9 to 2.11. After completing cluster analysis, we have collected those glycan structures where the value of RFU, STDEV, and SEM has been significantly changed from normal state to infected state.

Figure 2.2 K-means cluster analysis of Influenza A (H1N1) non-infected human.

Figure 2.3 K-means cluster analysis of Influenza A (H1N1) infected human.

Figure 2.4 K-means cluster analysis of Influenza A (H1N1) infected human.

Figure 2.5 K-means cluster analysis of Influenza A (H1N1) infected human.