A Deterministic Hitting-Time Moment Approach to Seed-set Expansion over a Graph
Abstract not provided.
Publications
Tallis: Nonlinear dimension reduction for heterogeneous data
Abstract not provided.
Dynamic Model Updating for Streaming Classification and Clustering
Abstract not provided.
Model based network clustering based on random walk hitting times
Abstract not provided.
Tallis: Dimension Reduction for Mixed-Type Data
Abstract not provided.
An Expert Test Bed for Nuclear Forensics Group Inclusion/Exclusion
Abstract not provided.
Distance Metrics and Clustering Methods for Mixed-type Data
International Statistical Review
In spite of the abundance of clustering techniques and algorithms, clustering mixed interval (continuous) and categorical (nominal and/or ordinal) scale data remain a challenging problem. In order to identify the most effective approaches for clustering mixed–type data, we use both theoretical and empirical analyses to present a critical review of the strengths and weaknesses of the methods identified in the literature. Here, the guidelines on approaches to use under different scenarios are provided, along with potential directions for future research.
7 Results