Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index

Clustering is a classic data mining technology, which has been widely used in pattern recognition, machine learning, artificial intelligence and other fields. Through clustering analysis, the deep structure of the target data set can be effectively discovered. As a commonly used partitioning and clu...

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Published in:Ji suan ji ke xue Vol. 49; no. 1; pp. 121 - 132
Main Authors: Zhang, Ya-di, Sun, Yue, Liu, Feng, Zhu, Er-zhou
Format: Journal Article
Language:Chinese
Published: Chongqing Guojia Kexue Jishu Bu 01.01.2022
Editorial office of Computer Science
Subjects:
Algorithms
Artificial intelligence
Cluster analysis
Clustering
clustering algorithm|clustering validity index|optimal clustering number|cluster center|data mining
Data mining
Datasets
Density
Machine learning
Outliers (statistics)
Parameters
Pattern recognition
ISSN:1002-137X
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Abstract Clustering is a classic data mining technology, which has been widely used in pattern recognition, machine learning, artificial intelligence and other fields. Through clustering analysis, the deep structure of the target data set can be effectively discovered. As a commonly used partitioning and clustering algorithm, K-means has the advantages of being simple to implement and capable of handling large data. However, affected by the convergence rules, the K-means algorithm is still very sensitive to the selection of initial cluster centers and cannot be It handles non-convex distributions and data sets with outliers well. An improved K-means algorithm DC-Kmeans based on density parameters and center replacement is proposed in this paper. The algorithm uses the density parameters of the data object to gradually Determine the initial cluster center, and use the center replacement method to update the initial center that deviates from the actual position, so it is more accurate than the traditional clustering alg
AbstractList Clustering is a classic data mining technology, which has been widely used in pattern recognition, machine learning, artificial intelligence and other fields. Through clustering analysis, the deep structure of the target data set can be effectively discovered. As a commonly used partitioning and clustering algorithm, K-means has the advantages of being simple to implement and capable of handling large data. However, affected by the convergence rules, the K-means algorithm is still very sensitive to the selection of initial cluster centers and cannot be It handles non-convex distributions and data sets with outliers well. An improved K-means algorithm DC-Kmeans based on density parameters and center replacement is proposed in this paper. The algorithm uses the density parameters of the data object to gradually Determine the initial cluster center, and use the center replacement method to update the initial center that deviates from the actual position, so it is more accurate than the traditional clustering alg
As a classical data mining technique,clustering is widely used in fields as pattern recognition,machine learning,artificial intelligence,and so on.By effective clustering analysis,the underlying structures of datasets can be identified.As a commonly used partitional clustering algorithm,K-means is simple of implementation and efficient on classifying large scale datasets.However,due to the influence of the convergence rule,the traditional K-means is still suffering problems as sensitive to the initial clustering centers,cannot properly process non-convex distributed datasets and datasets with outliers.This paper proposes the DC-Kmeans (density parameter and center replacement K-means),an improved K-means algorithm based on the density parameter and center replacement.Due to the gradually selecting of initial clustering centers and continuously update imprecision old centers,the DC-Kmeans is more accurate than the traditional K-means.Two novel methods are also proposed for optimally clustering:1)a novel cluste
Author Sun, Yue
Liu, Feng
Zhang, Ya-di
Zhu, Er-zhou
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Snippet Clustering is a classic data mining technology, which has been widely used in pattern recognition, machine learning, artificial intelligence and other fields....
As a classical data mining technique,clustering is widely used in fields as pattern recognition,machine learning,artificial intelligence,and so on.By effective...
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SubjectTerms Algorithms
Artificial intelligence
Cluster analysis
Clustering
clustering algorithm|clustering validity index|optimal clustering number|cluster center|data mining
Data mining
Datasets
Density
Machine learning
Outliers (statistics)
Parameters
Pattern recognition
Title Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index
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