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Review of clustering technology and its application in coordinating vehicle subsystems
Clustering is an unsupervised learning technology, and it groups information (observations
or datasets) according to similarity measures. Develo** clustering algorithms is a hot topic …
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 …
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
We use dynamic time war**, a non-parametric pattern recognition method, to study
interlinkages between major energy and agricultural commodity prices. Cluster analysis is …
interlinkages between major energy and agricultural commodity prices. Cluster analysis is …
Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models
Due to increasing environmental concerns and demand for clean energy resources,
photovoltaic (PV) systems are becoming more prevalent. Considering that in several …
photovoltaic (PV) systems are becoming more prevalent. Considering that in several …
Time2Feat: Learning interpretable representations for multivariate time series clustering
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 …
multiple signals and sensors. Existing systems aim to maximize effectiveness, efficiency and …
Trendlets: A novel probabilistic representational structures for clustering the time series data
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 …
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
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 …
Intelligence for IT Operations (AIOps) when the number of KPIs is large. This approach can …
Solar flare prediction using multivariate time series decision trees
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 …
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 …
learning and other fields. It has the advantages of simple thought, good effect and easy …
Evaluating CodeClusters for effectively providing feedback on code submissions
Full research paper—Most introductory programming courses rely on the use of automated
assessment for grading programming assignments. While such systems save teachers' time …
assessment for grading programming assignments. While such systems save teachers' time …