Abstract: Exploratory data analysis and dimension reduction techniques serve as the backbone for developing visual tools designed to foster new ideas and an understanding of the underlying phenomenon of interest. The tour is a dynamic visualization technique for exploratory data analyses of high-dimensional numeric data consisting of a smooth sequence of d-dimensional projections from p-dimensional Euclidean space. Combining a method for basis selection and geodesic interpolation between pairs of bases constructs the sequence of projections and results in a smooth progression of intermediate interpolation planes between target planes. We present a new type of tour that incorporates novelty detection, providing a versatile approach to unsupervised dimension reduction. The method fuses multiple projection pursuit indices while maintaining a short memory, circumventing the issues encountered with the randomness of the grand tour and the narrow view of the projection pursuit optimization. We evaluate the behavior of the method with simulated data, and the results highlight the flexibility of the method. This work expands visual methods for exploring diverse types of high-dimensional data.