Clustering by the way of atomic fission

Clustering by the way of atomic fission

Citation Author(s):
Shizhan
Lu
Submitted by:
Shizhan Lu
Last updated:
Tue, 07/23/2019 - 06:03
DOI:
10.21227/6fey-tw67
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Abstract: 

Cluster analysis which focuses on the grouping and categorization of similar elements is widely used in various fields of research. Inspired by the phenomenon of atomic fission, a novel density-based clustering algorithm is proposed in this paper, called fission clustering (FC). It focuses on mining the dense families of a dataset and utilizes the information of the distance matrix to fissure  clustering dataset into subsets. When we face the dataset which has a few points surround the dense families of clusters, K-nearest neighbors local density indicator is applied to distinguish and remove the points of sparse areas so as to obtain a dense subset that is constituted by the dense families of clusters.  A number of frequently-used datasets were used to test the performance of this clustering approach, and to compare the results with those of algorithms. The proposed algorithm is found to outperform other algorithms in speed and accuracy.

Instructions: 

Cluster analysis which focuses on the grouping and categorization of similar elements is widely used in various fields of research. Inspired by the phenomenon of atomic fission, a novel density-based clustering algorithm is proposed in this paper, called fission clustering (FC). It focuses on mining the dense families of a dataset and utilizes the information of the distance matrix to fissure  clustering dataset into subsets. When we face the dataset which has a few points surround the dense families of clusters, K-nearest neighbors local density indicator is applied to distinguish and remove the points of sparse areas so as to obtain a dense subset that is constituted by the dense families of clusters.  A number of frequently-used datasets were used to test the performance of this clustering approach, and to compare the results with those of algorithms. The proposed algorithm is found to outperform other algorithms in speed and accuracy.

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[1] Shizhan Lu, "Clustering by the way of atomic fission", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/6fey-tw67. Accessed: Aug. 23, 2019.
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doi = {10.21227/6fey-tw67},
url = {http://dx.doi.org/10.21227/6fey-tw67},
author = {Shizhan Lu },
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title = {Clustering by the way of atomic fission},
year = {2019} }
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T1 - Clustering by the way of atomic fission
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Shizhan Lu. (2019). Clustering by the way of atomic fission. IEEE Dataport. http://dx.doi.org/10.21227/6fey-tw67
Shizhan Lu, 2019. Clustering by the way of atomic fission. Available at: http://dx.doi.org/10.21227/6fey-tw67.
Shizhan Lu. (2019). "Clustering by the way of atomic fission." Web.
1. Shizhan Lu. Clustering by the way of atomic fission [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/6fey-tw67
Shizhan Lu. "Clustering by the way of atomic fission." doi: 10.21227/6fey-tw67