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The support vector machines (SVMs) is one of the machine learning algorithms with high classification accuracy. However, the support vector machine algorithm has a very high training complexity. Thus, it is not very efficient with large datasets. In this study, we have used the multi-class support vector machines and systematic sampling with hierarchical clustering (SSHC-MCSVM) algorithm for gene expression data classification. The gene expression profiles are considered as large datasets. The gene expression datasets that are used in this study are two datasets for obese and lean individuals. In this proposed (SSHC-MCSVM) algorithm, the gene expression data are regrouped to new sets of genes based on systematic sampling with hierarchical clustering (SSHC) algorithm. The SSHC algorithm repeated n times and the k-partitions with clusters that have high adjusted Rand index (ARI) are chosen. The multi-class support vector machines are applied to the best regrouped gene expression data to classify the significant genes. The performance measures are accuracy, recall, and precision. The proposed algorithm which is SSHC-MCSVM could classify the significant genes with high accuracy, recall, and precision.

Microarray datasets are enriched with numerous unknown gene expression patterns that may have significant biological meaning. Detecting well-separated gene expression patterns is a critical task in microarray data analysis. The density-based spatial clustering DBSCAN algorithm has been used to detect patterns with different shapes and sizes in many applications. However, the DBSCAN algorithm is time-consuming when used on big datasets, and microarray datasets are considered as big and complex datasets. Therefore, in this study, we modified the DBSCAN algorithm by combining it with a partitioning around medoids algorithm based on normalized and weighted Mahalanobis distance (NWM). The developed algorithm (NWM_PDBSCAN) was tested on selected microarray expression datasets, which were pre-processed prior to analysis. The results revealed an optimal cluster solution with different shapes and sizes. We further reduced the dataset sizes using a random sampling technique to enhance the performance of the DBSCAN algorithm. The proposed NWM_PDBSCAN algorithm performed ideally, and was evaluated using Dunn’s validity index. Keywords: Microarray data; Partitioning around medoids; DBSCAN; Normalized weighted Mahalanobis distance; Validity index; Pre-processing; Sampling; Number of clusters

Microarray datasets are enriched with numerous unknown gene expression patterns that may have significant biological meaning. Detecting well-separated gene expression patterns is a critical task in microarray data analysis. The density-based spatial clustering DBSCAN algorithm has been used to detect patterns with different shapes and sizes in many applications. However, the DBSCAN algorithm is time-consuming when used on big datasets, and microarray datasets are considered as big and complex datasets. Therefore, in this study, we modified the DBSCAN algorithm by combining it with a partitioning around medoids algorithm based on normalized and weighted Mahalanobis distance (NWM). The developed algorithm (NWM_PDBSCAN) was tested on selected microarray expression datasets, which were pre-processed prior to analysis. The results revealed an optimal cluster solution with different shapes and sizes. We further reduced the dataset sizes using a random sampling technique to enhance the performance of the DBSCAN algorithm. The proposed NWM_PDBSCAN algorithm performed ideally, and was evaluated using Dunn’s validity index. Keywords: Microarray data; Partitioning around medoids; DBSCAN; Normalized weighted Mahalanobis distance; Validity index; Pre-processing; Sampling; Number of clusters, Journal of Information and Communication Engineering (JICE)