The Border K-Means Clustering Algorithm for One Dimensional Data

Clustering has been widely used for data pre-processing, mining, and analysis. The k-means clustering algorithm is commonly used because of its simplicity and flexibility to work in many real-life applications and services. Despite being commonly used, the k-means algorithm suffers from non-determin...

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Veröffentlicht in:International Conference on Big Data and Smart Computing S. 35 - 42
Hauptverfasser: Froese, Ryan, Klassen, James W., Leung, Carson K., Loewen, Tyler S.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2022
Schlagworte:
Big data
cluster analysis
clustering
Clustering algorithms
Conferences
data analysis
data mining
Data visualization
Earth
k-means algorithm
machine learning
Runtime
smart computing
Visual analytics
ISSN:2375-9356
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Abstract Clustering has been widely used for data pre-processing, mining, and analysis. The k-means clustering algorithm is commonly used because of its simplicity and flexibility to work in many real-life applications and services. Despite being commonly used, the k-means algorithm suffers from non-deterministic results and run times that greatly vary depending on the initial selection of cluster centroids. To improve the k-means algorithm, we present in this paper a border k-means clustering algorithm. It combines concepts from the k-means algorithm with an additional focus on the concepts of the borders dividing clusters. Consequently, the resulting border k-means algorithm leads to deterministic results and a great reduction in run time when compared with the traditional k-means algorithm.
AbstractList Clustering has been widely used for data pre-processing, mining, and analysis. The k-means clustering algorithm is commonly used because of its simplicity and flexibility to work in many real-life applications and services. Despite being commonly used, the k-means algorithm suffers from non-deterministic results and run times that greatly vary depending on the initial selection of cluster centroids. To improve the k-means algorithm, we present in this paper a border k-means clustering algorithm. It combines concepts from the k-means algorithm with an additional focus on the concepts of the borders dividing clusters. Consequently, the resulting border k-means algorithm leads to deterministic results and a great reduction in run time when compared with the traditional k-means algorithm.
Author Leung, Carson K.
Froese, Ryan
Loewen, Tyler S.
Klassen, James W.
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  surname: Froese
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  organization: University of Manitoba,Department of Computer Science,Winnipeg,MB,Canada
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  givenname: James W.
  surname: Klassen
  fullname: Klassen, James W.
  organization: University of Manitoba,Department of Computer Science,Winnipeg,MB,Canada
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  givenname: Carson K.
  surname: Leung
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  email: kleung@cs.umanitoba.ca
  organization: University of Manitoba,Department of Computer Science,Winnipeg,MB,Canada
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  givenname: Tyler S.
  surname: Loewen
  fullname: Loewen, Tyler S.
  organization: University of Manitoba,Department of Computer Science,Winnipeg,MB,Canada
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Snippet Clustering has been widely used for data pre-processing, mining, and analysis. The k-means clustering algorithm is commonly used because of its simplicity and...
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StartPage 35
SubjectTerms Big data
cluster analysis
clustering
Clustering algorithms
Conferences
data analysis
data mining
Data visualization
Earth
k-means algorithm
machine learning
Runtime
smart computing
Visual analytics
Title The Border K-Means Clustering Algorithm for One Dimensional Data
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