This paper presents the first time series clustering benchmark utilizing all time series
datasets currently available in the University of California Riverside (UCR) archive—the …

Local energy markets (LEMs) are utilized in a bottom-up power systems approach for
reducing the complexity of the traditional, centralized power system and to enable better …

Correctly defining and grou** electrical feeders is of great importance for electrical system
operators. In this paper, we compare two different clustering techniques, K-means and …

Abstract Learning to Rank (L2R) improves ranking quality but relies on the existence of
manually labeled training sets, which are expensive and cumbersome to generate. Using …

Active learning is an effective approach for tasks with limited labeled data. It samples a small
set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative …

Persistent and quality graduation rates of students are increasingly important indicators of
progressive and effective educational institutions. Timely analysis of students' data to guide …

Effective optimization is essential for interactive systems to provide a satisfactory user
experience. However, it is often challenging to find an objective to optimize for. Generally …

Cluster analysis explores the underlying structure of data and organizes it into groups (ie,
clusters) such that observations within the same group are more similar than those in …

This research used mixed methods to explore how universities may incorporate profiling into
their mental health support packages by creating student mental health profiles to target and …

Local energy markets (LEMs) are utilized in a bottom-up power systems approach for
reducing the complexity of the traditional, centralized power system and to enable better …