## 1 Introduction  
Analysis of high-dimensional data, with more features (\(p\)) than observations (\(N\)) (\(p>>N\)), places significant demand in cost and memory computational usage attributes. Analysis of high-dimensional data requires both high computational costs and computer memory usage [1]. Dimensionality reduction techniques that minimize the features in the original data without losing any important information have been used to address this challenge [2]. This reduces storage space and computational time and removes redundant, noisy and irrelevant data while improving algorithm efficiency as well as accuracy [3]. Feature selection and extraction constitute common approaches used to reduce dimensionality with the former identifying subsets of sufficient informative features that define the data. Major feature-selection techniques include filters, wrappers and embedded/hybrid techniques [4]. Wrappers follow a greedy search approach that wraps the feature selection around a learning algorithm and subsequently uses the accuracy of performance or error rate of the classification process as a criterion of feature evaluation. However, this approach is slower for a large feature space because every feature must be evaluated using a trained classifier. The filter method checks the features relying on the intrinsic characteristics (information, dependency, consistency and distance) prior to the learning tasks [5], which requires lower computational resources. Filters are thus faster but inefficient in classification relative to wrapper methods. Hybrid/embedded techniques are a combination of filters and wrappers [6]. We refer readers to the following articles and references therein for detailed reviews of feature-selection methods [7; 8; 9; 10; 11; 12; 13]. Feature-extraction techniques obtain the most important features from a dataset [14]. Principal component analysis (PCA), for example, uses covariance to extract relevant features from high-dimensional data [15]. A major drawback of feature-extraction techniques is their inaccuracy when mapping from a high-dimensional space to a low-dimensional space, which leads to the loss of data interpretability [16]. Another approach used in the analysis of big data is machine learning which has been defined as the ability of machines to learn without being programmed [17]. Classification is a supervised machine learning approach where labels are provided with the input data. Trained classifiers have been used in areas such as diabetes studies [18], cancer studies [19] and medical diagnosis [14]. Metrics such as accuracy, sensitivity, specificity, recall, robustness, computational scalability and computational cost are then used to evaluate the performance of the classifiers [20]. Microarray and next-generation sequencing (NGS) are two high-throughput technologies that generate large volumes of high-dimensional biological data [21]. Discovering meaningful associations in these kinds of data is time-consuming and computationally demanding. Association rule mining [22] is a data mining approach that has been widely used to discover high-frequency co-occurrence of items in databases. The Apriori algorithm iteratively finds associations among or between features in a dataset [23]. A major advantage of this method is that it does not require prior knowledge (training) on the dataset. However, a discretization step is required to transform continuous variables into one-hot encoding, a data structure recognized by Apriori. When biological data are analyzed using this approach, the output of association rule mining reflects expected biological associations between different features. In this study, we highlighted the effects of various feature-selection methods on classification and association rule mining. Based on the results, we recommend a graph-based feature-selection method as a more suitable dimensionality reduction strategy when selecting features that can be used for classification and association rule mining from high-dimensional RNA-Seq data.