I-niceMO Enhanced Algorithm Based on Intersection Angel Geometry

doi:10.16182/j.issn1004731x.joss.21-1333

Abstract

Abstract:

To exactly determine the number of cluster centers and correctly identify the candidate cluster centers, an I-niceMO enhanced(I-niceMOEn) algorithm based on intersection angel geometry is proposed. As many distributions of intersection angles and distances as possible between observation points and data points are utilized to recognize the candidate cluster centers to avoid the neglection of cluster centers. The spectral clustering algorithm is used to automatically merge the candidate cluster centers according to the eigenvalues of Laplacian matrices. The number of final cluster centers is determined by the number of merged candidate cluster centers. The number of clusters can be automatically determined by I-niceMOEn algorithm and the manual parameter input for clustering is not needed. The experimental results show that I-niceMOEn algorithm is convergent and outperforms the traditional automatic clustering methods and I-niceMO algorithm.

Key words: automatic clustering, I-nice clustering, spectral clustering, unsupervised learning, observation point mechanism

CLC Number:

TP391

Yifan He, Yulin He, Yongda Cai, Zhexue Huang. I-niceMO Enhanced Algorithm Based on Intersection Angel Geometry[J]. Journal of System Simulation, 2023, 35(4): 797-808.

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数据集	数据量	类别数	维度
Wine	178	3	13
Iris	150	3	4
Breast	569	2	30

数据集	类别数	Elbow	Silhouette	I-niceSO	I-niceMO	I-niceMOEn
Wine	3	7	2	2	5	3
Iris	3	6	2	2	4	3
Breast	2	6	2	4	6	2

数据集	类别数	I-niceSO/%	I-niceMO/%	I-niceMOEn/%
S1	10	72.9	87.1	100
S2	20	79.1	100	98.6
S3	30	82.6	100	99.3
S4	40	82.4	92.8	98.5
S5	10	91.0	100	100
S6	20	83.8	73.5	100
S7	30	76.3	100	100
S8	40	49.6	97.6	100

数据集	类别数	I-niceSO/%	I-niceMO/%	I-niceMOEn/%
Wine	3	68.5	85.0	96.0
Iris	3	66.6	95.0	88.0
Breast	2	87.7	85.9	89.5