Biclustering is a simultaneous clustering technique by finding sub-matrixes that have the same similarity between rows and columns. One of the biclustering algorithms that is relatively fast and can be used as a reference for the comparison of several algorithms is the BCBimax algorithm. The BCBimax algorithm works by finding a sub-matrix containing element 1 of the formed binary data matrix. The selection of thresholds in the binarization process and the minimum combination of rows and columns are essential in finding the optimal bicluster. Capture fisheries have an important role in supporting sustainable growth in Indonesia, so information on the potential of fish species that have similarities in several provinces is needed in optimally mapping the potential. The BCBimax algorithm found 11 optimal biclusters in grouping capture fisheries data. The median of each variable is used as a threshold in the binarization process, and the minimum combination of row 2 and maximum column 2 is chosen to find the optimal bicluster result. The optimal average value of Mean Square Residual bicluster obtained is 0.405403 with the similarity of bicluster results (Liu and Wang index) which is different for each bicluster combination produced. All the bicluster results grouped the provinces and types of fish that had the same potential simultaneously.

Biclustering is an analytical tool to group data from two dimensions simultaneously. The analysis was first introduced by Hartigan (1972) and applied by Cheng and Church (2000) to the gene expression matrix. The Cheng and Church (CC) algorithm is a popular biclustering algorithm and has been widely applied outside the field of biological data in recent years. This algorithm application in economic and Covid-19 pandemic vulnerability cases is exciting and essential to do in order to get an overview of the spatial pattern and characteristics of the bicluster of economic and COVID-19 pandemic vulnerability in Indonesia. This study uses secondary data from some ministries. Forming a bicluster using the CC algorithm requires determining the delta threshold so that several types of delta thresholds are formed to choose the best (optimum) using the evaluation of the average value of mean square residue (MSR) to volume ratios. The similarity of the optimum bi-cluster with the other is also seen based on the Liu and Wang index values. The 0.01 delta threshold is chosen as the optimum threshold because it produces the smallest average value of MSR to volume ratios (0.00032). Based on Liu and Wang Index values, the optimum threshold has a similarity level below 50% with other types of delta thresholds, so the threshold is the best unique threshold. The optimum threshold resulted in six biclusters (six spatial patterns). Most regions in Indonesia (11 provinces) tend to have low economic and COVID-19 pandemic vulnerability in the first spatial pattern characteristic variables.

Bi-clustering is a clustering development that aims to group data simultaneously from two directions. The Iterative Signature Algorithm (ISA) is one of the bi-clustering algorithms that work iteratively to find the most correlated bi-cluster. Detecting economic and pandemic vulnerability using bi-cluster analysis is essential to get spatial patterns and an overview of Indonesia's economic and pandemic vulnerability characteristics. Bi-clustering using ISA requires setting the row and column threshold to form seventy combinations of thresholds. The best is chosen based on the average value of mean square residue to volume ratios. In addition, the similarity of the best bi-cluster with the other is also seen based on the Liu and Wang index values. The -1.0 row and -1.0 column threshold combinations were selected and produced the best bi-cluster with the smallest average value of mean square residue to volume ratios (0.00141). Based on Liu and Wang index values, it has more than 95% similarity with the combination of -1.0 row and -0.9 column thresholds and the -0.9 row and -1.0 column thresholds. These selected threshold combinations produce three bi-clusters with five types of spatial patterns and different characteristics because of the overlap between these three bi-clusters.