Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Classification"
DOI: 10.1007/s00357-018-9296-4
Abstract: Fast centroid-based clustering algorithms such as k-means usually converge to a local optimum. In this work, we propose a method for constructing a better clustering from two such suboptimal clustering solutions based on the fact… read more here.
Keywords: based clustering; using suitable; improving centroid; clustering using ... See more keywords
Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function
Sign Up to like & getrecommendations! Published in 2019 at "Soft Computing"
DOI: 10.1007/s00500-019-03792-z
Abstract: This paper proposes the use of fuzzification functions based on clustering of data based on their density to perform the granularization of the input space. The neurons formed in this layer are built through the… read more here.
Keywords: based clustering; layer; activation function; density ... See more keywords
Constraint-based clustering selection
Sign Up to like & getrecommendations! Published in 2017 at "Machine Learning"
DOI: 10.1007/s10994-017-5643-7
Abstract: Clustering requires the user to define a distance metric, select a clustering algorithm, and set the hyperparameters of that algorithm. Getting these right, so that a clustering is obtained that meets the users subjective criteria,… read more here.
Keywords: constraint based; selection; constraint; based clustering ... See more keywords
An information entropy based-clustering algorithm for heterogeneous wireless sensor networks
Sign Up to like & getrecommendations! Published in 2020 at "Wireless Networks"
DOI: 10.1007/s11276-018-1877-y
Abstract: This paper proposes a novel dynamic, distributive, and self-organizing entropy based clustering scheme that benefits from the local information of sensor nodes measured in terms of entropy and use that as criteria for cluster head… read more here.
Keywords: based clustering; networks information; entropy based; wireless ... See more keywords
Density-based clustering of big probabilistic graphs
Sign Up to like & getrecommendations! Published in 2019 at "Evolving Systems"
DOI: 10.1007/s12530-018-9223-2
Abstract: Clustering is a machine learning task to group similar objects in coherent sets. These groups exhibit similar behavior with-in their cluster. With the exponential increase in the data volume, robust approaches are required to process… read more here.
Keywords: based clustering; density based; graphs; probabilistic graphs ... See more keywords
Energy paths in the European Union: A model-based clustering approach
Sign Up to like & getrecommendations! Published in 2017 at "Energy Economics"
DOI: 10.1016/j.eneco.2017.05.014
Abstract: This paper examines typical “energy paths”, i.e. the intertemporal development of the energy mixes of the member states of the European Union over 1971–2010. We apply model-based clustering to detect major energy profiles and their… read more here.
Keywords: energy paths; model based; energy; european union ... See more keywords
Electricity market transitions in Australia: Evidence using model-based clustering
Sign Up to like & getrecommendations! Published in 2021 at "Energy Economics"
DOI: 10.1016/j.eneco.2021.105590
Abstract: Abstract We examine the energy profiles and paths of the participating states of Australia's National Electricity Market between 2011 and 2019, using a model-based clustering approach. We identify 25 distinct electricity generation clusters or profiles,… read more here.
Keywords: model based; electricity market; electricity; generation ... See more keywords
First Results in Leak Localization in Water Distribution Networks using Graph-Based Clustering and Deep Learning
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.1104
Abstract: Abstract This paper presents a methodology for the localization of leaks in water distribution networks (WDNs) by means of the combination of a deep learning (DL) approach and a graph-based clustering technique. A data set… read more here.
Keywords: based clustering; distribution networks; water distribution; graph based ... See more keywords
A systematic density-based clustering method using anchor points
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.02.119
Abstract: Abstract Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a… read more here.
Keywords: clustering method; anchor points; method; intermediate clusters ... See more keywords
Optimizing photovoltaic power plant site selection using analytical hierarchy process and density-based clustering – Policy implications for transmission network expansion, Ghana
Sign Up to like & getrecommendations! Published in 2021 at "Sustainable Energy Technologies and Assessments"
DOI: 10.1016/j.seta.2021.101521
Abstract: Abstract Despite the enormous solar energy resource in Ghana, the country is yet to fully take advantage of this resource to meet the country’s growing energy demand. Some studies have partly associated this to the… read more here.
Keywords: based clustering; density based; process density; transmission network ... See more keywords
Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images.
Sign Up to like & getrecommendations! Published in 2017 at "Analytical chemistry"
DOI: 10.1021/acs.analchem.7b01758
Abstract: Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach… read more here.
Keywords: graph based; clustering methods; two phase; phase graph ... See more keywords