# Clustering Analysis for Classifying Student Academic Performance in Higher Education
## 3 Methodology
### Proposed Clustering Methodology  
In recent years, the effectiveness of the use of clustering techniques in student performance prediction studies has attracted the interest of many researchers. The clustering technique refers to one method of grouping several similar objects into one cluster while different objects into another. The clustering technique will be very useful if the labelled information from students in the dataset is unknown. In addition, the division of large data sets into small, logical clusters will make it easier for researchers to examine and explain the meaning of the data.  
**K-means Algorithm**. The researchers' main choice is the k-means algorithm, a popular clustering technique. This technique is popular because the way it is implemented is very simple, and the results are also easy to understand. The k-means algorithm is a method for grouping nearby objects into the \(k\) number of the centroid. The elbow method is a popular way to figure out the best number of clusters. When given several clusters, k, this approach calculates the total of the within-cluster variance, also known as inertia, and then shows the variance curve concerning \(k\). The best number of clusters could be the k value at the curve's initial turning point.  
The alternative technique is to use silhouette plot analysis by calculating the coefficients for each data point to measure its similarity with its cluster as compared to other clusters. The value of the silhouette coefficient is in the range [1,\(-\)1] where a high value indicates that the object is well matched to its cluster.  
**BIRCH Algorithm**. The BIRCH algorithm is an agglomerate hierarchical clustering technique that excels at huge datasets with high dimensionality. By aggregating the cluster's zero, first, and second moments, BIRCH generates a height-balanced clustering feature tree of nodes that summarises data. The clustering feature (CF) produced is utilised to determine the centroids and quantify the cluster's compactness and distance. This storage of statistical information in the CF, such as the number of data points, the linear sum of \(N\) points, and the sum of squares of \(N\) points, reduces the number of recalculations and allows for incremental sub-cluster merging.  
**DBSCAN Algorithm**. One of the most popular algorithms in the density clustering category is DBSCAN, which was introduced by [PERSON], [PERSON], [PERSON], and [PERSON] in 1996. This algorithm separates the data points into three parts. The first part is the main point which is the points that are in the cluster. The second part is the boundary points which are the points that fall into the neighbourhood of the main point. The last part is the noise, which is the points not included in the main and boundary points. DBSCAN is very sensitive to the setting of epsilon parameters where a small value will cause the resulting clusters to be categorized as noise. At the same time, the change to a larger value will cause the clusters to be merged and become denser. DBSCAN does not require setting the number of clusters at the start-up phase of the algorithm.