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IEEE Transactions on Power Systems
The accuracy and reliability of the Q/V sensitivity for voltage security assessment is influenced by the outliers present in the calculation results. An unsupervised machine learning approach, empirical- cumulative- distribution- based outlier detection (
Han Gao +3 more
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The accuracy and reliability of the Q/V sensitivity for voltage security assessment is influenced by the outliers present in the calculation results. An unsupervised machine learning approach, empirical- cumulative- distribution- based outlier detection (
Han Gao +3 more
semanticscholar +1 more source
Intelligent recommender system based on unsupervised machine learning and demographic attributes
Simulation modelling practice and theory, 2021Recommendation systems aim to predict users interests and recommend items most likely to interest them. In this paper, we propose a new intelligent recommender system that combines collaborative filtering (CF) with the popular unsupervised machine ...
Yassine Afoudi +2 more
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A Review on Analysis of K-Means Clustering Machine Learning Algorithm based on Unsupervised Learning
Journal of Artificial Intelligence and SystemsThe process of machine learning is understood within Artificial Intelligence. Machine learning process gives the tools the ability to learn from their experiences and improve themselves without any coding.
Manish Suyal, Sanjay Sharma
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Journal of Physical Chemistry Letters, 2021
Using unsupervised machine learning on the trajectories from a nonadiabatic molecular dynamics simulation with time-dependent Kohn-Sham density functional theory, we elucidated the structural parameters with the largest influence on nonradiative ...
Spencer M Mangan +3 more
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Using unsupervised machine learning on the trajectories from a nonadiabatic molecular dynamics simulation with time-dependent Kohn-Sham density functional theory, we elucidated the structural parameters with the largest influence on nonradiative ...
Spencer M Mangan +3 more
semanticscholar +1 more source
2017
This chapter explores two important concepts of unsupervised machine learning: clustering and association rules. Clustering is an unsupervised data-analysis technique used to identify hidden patterns in data. Clustering is also part of exploratory analysis, used to understand data and its properties and to identify any outliers that exist.
Umesh R. Hodeghatta, Umesh Nayak
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This chapter explores two important concepts of unsupervised machine learning: clustering and association rules. Clustering is an unsupervised data-analysis technique used to identify hidden patterns in data. Clustering is also part of exploratory analysis, used to understand data and its properties and to identify any outliers that exist.
Umesh R. Hodeghatta, Umesh Nayak
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2008
In this chapter we explore the use of unsupervised machine learning, or clustering. We cover distances, dimension reduction techniques, and a variety of unsupervised machine learning methods including hierarchical clustering, k-means clustering, and specialized methods, such as those in the hopach package.
R. Gentleman, V. J. Carey
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In this chapter we explore the use of unsupervised machine learning, or clustering. We cover distances, dimension reduction techniques, and a variety of unsupervised machine learning methods including hierarchical clustering, k-means clustering, and specialized methods, such as those in the hopach package.
R. Gentleman, V. J. Carey
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E-commerce Customer Segmentation via Unsupervised Machine Learning
International Conference on Computing and Data Science, 2021Customer segmentation through data mining could help companies conduct customer-oriented marketing and build differentiated strategies targeted at diverse customers.
Boyu Shen
semanticscholar +1 more source
Unsupervised Machine Learning Methods for Artifact Removal in Electrodermal Activity
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021Artifact detection and removal is a crucial step in all data preprocessing pipelines for physiological time series data, especially when collected outside of controlled experimental settings.
S. Subramanian +3 more
semanticscholar +1 more source
2019
As the name suggests, unsupervised machine learning does not include finding relationships between input and output. To be honest, there is no output that we try to predict in unsupervised learning. It is mainly used to group together the features that seem to be similar to one another in some sense.
openaire +1 more source
As the name suggests, unsupervised machine learning does not include finding relationships between input and output. To be honest, there is no output that we try to predict in unsupervised learning. It is mainly used to group together the features that seem to be similar to one another in some sense.
openaire +1 more source
Distributed unsupervised learning using the multisoft machine
Information Sciences, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
G. PATANE', RUSSO, Marco
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