Cluster Analysis

This chapter covers cluster analysis, a set of methods used for identifying groups of similar observations based on proximity measures. The chapter focuses on specific methods KNIME, a data analytics platform. Cluster analysis aims to find groups of objects that are similar within each group and distinct from objects in other groups. The number and composition of clusters can be challenging to determine, making cluster analysis an unsupervised and descriptive technique. Hierarchical clustering is a flexible method that can work with different distance measures and linkage types. It forms a tree-like structure of clusters, starting from individual observations and gradually merging them. On the other hand, K-means clustering, requires specifying the number of clusters beforehand and aims to minimize within-cluster variance by iteratively updating cluster centroids. Density-based clustering, like DBSCAN, can discover arbitrarily shaped clusters and is robust to outliers. Fuzzy clustering assigns probabilistic membership to clusters, allowing observations to belong partially to multiple clusters. Cluster validation is the process of evaluating the quality of clustering results. Internal validation uses metrics derived from the data, such as the Silhouette coefficient or within-cluster sum of squares. External validation compares the clustering results with external criteria or expected patterns. Overall, cluster analysis is a powerful technique for grouping similar observations, but it requires careful consideration of algorithm choice, parameter settings, and validation to produce meaningful and useful clusters.