This paper analyzes the noise-free but highly overlapped and imbalanced data set involving six analytes. We explored the principal components in the feature space to observe which spectral bands contribute the most contrast or data spreading. We also compared the principal component analysis (PCA) results and the corresponding k-means clustering algorithm results generated using lower PCs, i.e. PC4 and PC5 to see how it compares to using a combination of top principal components (PCs), i.e. PC1 and PC2. Specifically, we revealed the data in PC1-PC2 space, PC1-PC3 space, PC2-PC3 space, and PC4-PC5 space, respectively. Then we used the K-mean clustering algorithm to classify them into six clusters. We developed an algorithm to automatically determine the classifiers associated with each cluster. The algorithm can accurately determine which clustering corresponds to which analyte. We also conducted the clustering performance evaluation by calculating the probability of detection (POD), false alarm rate (FAR), accuracy, precision, and recall. This process was facilitated by developing an automatic algorithm to determine the true positive (TP), false negative (FN), false positive (FP), and true negative (TN), which are components of the above performance evaluation matrices. In addition, we compared the clustering abilities with different combination of principal components. Moreover, we investigated the false alarms to see if they are always the same samples, and if so, which ones. The experimental results demonstrated that top principal components (PCs) have higher clustering accuracy than the lower PCs. In addition, the principal component analysis and K-means clustering algorithm are efficient algorithms for achieving dimensional reduction and clustering on the high dimensional and imbalanced data set.