## 6 Discussion  
We have developed a two-part advanced analysis scheme, involving \"i-means\" and market basket analyses (Figure 1). The i-means step includes two machine learning algorithms, \"K-means clustering\" and \"random forest.\" While K-means is a well-known clustering algorithm, the appropriate number of categories is usually not known and the result depends on the initial point of the centroids, which is known as the initialization trap [33]. To overcome the problem of selecting an appropriate number of groups, we referred to previous reports [22; 31; 32]. To address the issue of the centroids, we checked the validity of initial centroid coordinates by comparing the random forest error rate to the K-means result. Using i-means, we were able to successfully divide a complicated dataset because the error rate from random forest improved with repeated i-means analyses (Figure S4). The plateauing of the error rate indicates that repeated i-means analysis might converge the best clusters of the dataset (Figure S5). The divided classes of NMR and Miseq datasets were able to be mined for unique variables and unique groups were identified (Figures S3 and S4). However, clustering the internal validation index, which \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Target** & **Support** & **Confidence** & **Lift** \\ \hline High\_Creatine & 0.98 & 1.00 & 1.02 \\ High\_Creatine.G3 GPC & 0.98 & 1.00 & 1.02 \\ High\_Gln\_Malic.acid & 0.53 & 1.00 & 1.02 \\ High\_Gly & 0.98 & 1.00 & 1.02 \\ High\_Ile & 0.98 & 1.00 & 1.02 \\ High\_Ile & 0.98 & 1.00 & 1.02 \\ High\_Iaclate & 0.41 & 1.00 & 1.02 \\ High\_Iaclate & 0.98 & 1.00 & 1.02 \\ High\_Leu & 0.98 & 1.00 & 1.02 \\ High\_malicacid & 0.16 & 1.00 & 1.02 \\ High\_Thr & 0.80 & 1.00 & 1.02 \\ High\_Val & 0.98 & 1.00 & 1.02 \\ Length2 (150–200 mm) & 0.31 & 1.00 & 1.02 \\ Low\_Actinobacteria & 0.11 & 1.00 & 1.02 \\ Low\_Cyanobacteria & 0.09 & 1.00 & 1.02 \\ Low\_Fusobacteria & 0.08 & 1.00 & 1.02 \\ Low\_Ile & 0.09 & 1.00 & 1.02 \\ Low\_Planctomycetes & 0.11 & 1.00 & 1.02 \\ Low\_Tenericutes & 0.07 & 1.00 & 1.02 \\ Low\_Tyr & 0.11 & 1.00 & 1.02 \\ NMRclass1 & 0.61 & 1.00 & 1.02 \\ NMRclass2 & 0.31 & 1.00 & 1.02 \\ Scales1 (Matte) & 0.17 & 1.00 & 1.02 \\ Season2 (June–August) & 0.22 & 1.00 & 1.02 \\ Season4 & 0.15 & 1.00 & 1.02 \\ (December–February) & 0.51 & 0.99 & 1.01 \\ Food6 (Fish eater) & 0.38 & 0.99 & 1.01 \\ Tail1 (Two distinct) & 0.36 & 0.99 & 1.01 \\ Season3 & 0.36 & 0.99 & 1.01 \\ (September–November) & 0.30 & 0.99 & 1.01 \\ Length1 (50–150 mm) & 0.30 & 0.99 & 1.01 \\ Bac\_class2 & 0.29 & 0.99 & 1.01 \\ Habitat (Offshore) & 0.29 & 0.99 & 1.01 \\ Color1 (Red) & 0.27 & 0.99 & 1.01 \\ Food5 (Mollusca eater) & 0.26 & 0.99 & 1.01 \\ Low\_Bacteria.Other & 0.23 & 0.99 & 1.01 \\ Habitat3 (Coast) & 0.43 & 0.99 & 1.00 \\ Ecology3 (Rockfish) & 0.21 & 0.98 & 1.00 \\ Scales3 (Shiny) & 0.41 & 0.98 & 1.00 \\ Length4 (Over 300 mm) & 0.18 & 0.98 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._is a well-known silhouette coefficient, did not correlate with the random forest error rate. This could mean that better clustering does not always provide meaningful separation to focus on unique characteristics of datasets (Table S2). The result was thought to be due to the data being from the field, not experimental, and they included several parameters and relevant variance but not ideal data distribution. This likely indicates that it is difficult to judge complicated data only by the clustering of the internal validation index, such as the silhouette coefficient. Additionally, qualitative data were able to be attached to classify parameters from i-means analysis and market basket analysis enabled the extraction of some association rules to the additional parameters from i-means classification (Figure 3B,C). These results demonstrate that our proposed scheme is suitable for identifying notable variables and proposing related parameters in challenging datasets.