In the context of Data Modelling and Analytics, which technique is most suitable for identifying the underlying patterns in high-dimensional data without explicitly labeling the data?

K-Means Clustering is a technique most suitable for identifying underlying patterns in high-dimensional data without the need for explicit labeling. It groups data into clusters based on similarity, where each cluster represents a pattern or structure in the data. K-Means is useful for exploratory data analysis to discover patterns or groupings within unlabelled data. Why Other Options are Wrong: a) Principal Component Analysis (PCA) reduces dimensionality but does not identify patterns or groupings. b) Linear Regression is a supervised learning technique used for predicting continuous values rather than identifying patterns in unlabelled data. d) Decision Trees are used for classification or regression tasks and require labelled data. e) Naive Bayes Classifier is a classification algorithm that also requires labelled data and does not identify patterns in unlabelled datasets.