• Publications
  • Influence
Data ultrametricity and clusterability
TLDR
A novel approach to determine the ultrametricity of a dataset is proposed via a special type of matrix product, which allows us to evaluate the clusterability of the dataset. Expand
  • 1
  • 1
  • PDF
Long-lead term precipitation forecasting by Hierarchical Clustering-based Bayesian Structural Vector Autoregression
TLDR
We apply a hierarchical clustering algorithm to generate new multivariate temporal data with new hydrometeorological values for a target location from a large spatiotemporal dataset and applied a BSVAR model to forecast precipitation using the generated data frame. Expand
  • 5
Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining
TLDR
We introduce a measure of ultrametricity for dissimilarity spaces and examine transformations of dissimilarities that impact this measure. Expand
  • 2
  • PDF
Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States
The existence of water markets establishes water prices, promoting trading of water from low- to high-valued uses. However, market participants can face uncertainty when asking and offering pricesExpand
  • 5
  • PDF
Clusterability, Model Selection and Evaluation
  • 1
Dual Criteria Determination of the Number of Clusters in Data
TLDR
We present a method for determining the number of clusters existent in a data set involving a bi-criteria optimization that makes use of the entropy and the cohesion of a partition. Expand