Final answer:
Automatic cluster detection techniques are a form of unsupervised learning and do not require a target variable; they are used to group similar data points in a dataset without the guidance of a known output variable.
Step-by-step explanation:
No, automatic cluster detection techniques do not require a target variable. Cluster detection falls under the category of unsupervised learning, which is used to identify patterns or groupings in data without the guidance of a known output variable (target variable). The goal of clustering is to divide a dataset into groups (clusters) such that data points in the same cluster are more similar to each other than to those in other clusters. This is fundamentally different from supervised learning methods, like decision trees, that do need a target variable to model the data.
In automatic cluster detection, algorithms such as K-means, hierarchical clustering, or DBSCAN are typically used to group data points based on similarity measures. These algorithms do not require any prior knowledge of the output categories and thus, no target variable is needed. The mentioned research using tRNA databases and decision-tree classifiers is an example of a supervised learning approach, which is distinct from automatic clustering methods.