Review of clustering technology and its application in coordinating vehicle subsystems

C Zhang, W Huang, T Niu, Z Liu, G Li, D Cao - Automotive Innovation, 2023‏ - Springer
Clustering is an unsupervised learning technology, and it groups information (observations
or datasets) according to similarity measures. Develo** clustering algorithms is a hot topic …

[HTML][HTML] Time-series clustering and forecasting household electricity demand using smart meter data

H Kim, S Park, S Kim - Energy Reports, 2023‏ - Elsevier
This study forecasts electricity consumption in a smart grid environment. We present a
bottom-up prediction method using a combination of forecasting values based on time …

On the linkages between energy and agricultural commodity prices: A dynamic time war** analysis

D Miljkovic, P Vatsa - International Review of Financial Analysis, 2023‏ - Elsevier
We use dynamic time war**, a non-parametric pattern recognition method, to study
interlinkages between major energy and agricultural commodity prices. Cluster analysis is …

Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models

R Nematirad, A Pahwa, B Natarajan… - Frontiers in Energy …, 2023‏ - frontiersin.org
Due to increasing environmental concerns and demand for clean energy resources,
photovoltaic (PV) systems are becoming more prevalent. Considering that in several …

Time2Feat: Learning interpretable representations for multivariate time series clustering

A Bonifati, F Del Buono, F Guerra… - Proceedings of the VLDB …, 2022‏ - hal.science
Clustering multivariate time series is a critical task in many realworld applications involving
multiple signals and sensors. Existing systems aim to maximize effectiveness, efficiency and …

Trendlets: A novel probabilistic representational structures for clustering the time series data

CI Johnpaul, MVNK Prasad, S Nickolas… - Expert Systems with …, 2020‏ - Elsevier
Time series data is a sequence of values recorded systematically over a period which are
mostly used for prediction, clustering, and analysis. The two essential features of a time …

A joint matrix factorization and clustering scheme for irregular time series data

S He, M Guo, Z Li, Y Lei, S Zhou, K **e, NN **ong - Information Sciences, 2023‏ - Elsevier
Abstract Key Performance Indicator (KPI) clustering plays an important role in Artificial
Intelligence for IT Operations (AIOps) when the number of KPIs is large. This approach can …

Solar flare prediction using multivariate time series decision trees

R Ma, SF Boubrahimi, SM Hamdi… - 2017 IEEE international …, 2017‏ - ieeexplore.ieee.org
Space Weather is of rising importance in scientific discipline that describes the way in which
the Sun and space impact a myriad of activities down on Earth as well as the safety of the …

Review on the research of K-means clustering algorithm in big data

C Jie, Z Jiyue, W Junhui, W Yusheng… - 2020 IEEE 3rd …, 2020‏ - ieeexplore.ieee.org
K-Means algorithm is an unsupervised learning algorithm, which is widely used in machine
learning and other fields. It has the advantages of simple thought, good effect and easy …

Evaluating CodeClusters for effectively providing feedback on code submissions

T Koivisto, A Hellas - 2022 IEEE Frontiers in Education …, 2022‏ - ieeexplore.ieee.org
Full research paper—Most introductory programming courses rely on the use of automated
assessment for grading programming assignments. While such systems save teachers' time …