Journal of System Simulation
Abstract
Abstract: The existing k-modes clustering method ignores the weak correlation of variable attributes, which often results in poor clustering performance in practical applications. A new k-modes clustering method that includes the weak correlation of attributes is proposed. Maximum information coefficient (MIC) is introduced to measure the correlation of variable attributes in the data set. The obtained MIC value is merged with the original distance to establish a new measurement method containing weak attribute correlation information to enhance the completeness of related information of variable attributes, and a more refined k-modes clustering method is established. Three different data sets are used to compare the performance of the new method with the existing k-modes clustering and other improved k-modes clustering methods, the simulation results shows the effectness of the new method.
Recommended Citation
Li, Mingmei; Wen, Chenglin; and Hu, Shaolin
(2022)
"A K-modes Clustering Method Based on Maximal Information Coefficient Data Preprocessing,"
Journal of System Simulation: Vol. 34:
Iss.
10, Article 10.
DOI: 10.16182/j.issn1004731x.joss.21-0484
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss10/10
First Page
2204
Revised Date
2021-08-06
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0484
Last Page
2212
CLC
TP311
Recommended Citation
Mingmei Li, Chenglin Wen, Shaolin Hu. A K-modes Clustering Method Based on Maximal Information Coefficient Data Preprocessing[J]. Journal of System Simulation, 2022, 34(10): 2204-2212.
DOI
10.16182/j.issn1004731x.joss.21-0484
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