IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering

Abstract

Scientists in many fields have the common and basic need of dimensionality reduction: visualizing the underlying structure of the massive multivariate data in a low-dimensional space. However, many dimensionality reduction methods confront the so-called "crowding problem" that clusters tend to overlap with each other in the embedding. Previously, researchers expect to avoid that problem and seek to make clusters maximally separated in the embedding. However, the proposed in-tree (IT) based method, called IT-map, allows clusters in the embedding to be locally overlapped, while seeking to make them distinguishable by some small yet key parts. IT-map provides a simple, effective and novel solution to cluster-preserving mapping, which makes it possible to cluster the original data points interactively and thus should be of general meaning in science and engineering.

13 pages, 6 figures

References in corpus (2)

• Clustering based on the In-tree Graph Structure and Affinity Propagation
• An Effective Semi-supervised Divisive Clustering Algorithm

Cited by in corpus (5)

• Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
• Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method
• Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
• IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family
• A general framework for the IT-based clustering methods