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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Bibliographic Details
Published in:International Conference on Big Data and Smart Computing pp. 35 - 42
Main Authors: Froese, Ryan, Klassen, James W., Leung, Carson K., Loewen, Tyler S.
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2022
Subjects:
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
Online Access:Get full text
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Summary: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.
ISSN:2375-9356
DOI:10.1109/BigComp54360.2022.00017