Energy method of geophysical logging lithology based on K-means dynamic clustering analysis

Environmental Technology & Innovation ◽

10.1016/j.eti.2021.101534 ◽

2021 ◽

pp. 101534

Author(s):

Jiankun Jing ◽

Shizhen Ke ◽

Tianjiang Li ◽

Tian Wang

Keyword(s):

Energy Method ◽

Clustering Analysis ◽

Dynamic Clustering ◽

Geophysical Logging

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Dynamic Clustering Analysis of Expert Instances with XML Structural Based on the Particle Swarm Algorithm

2010 International Conference on Management and Service Science ◽

10.1109/icmss.2010.5577291 ◽

2010 ◽

Author(s):

Jun Zeng

Keyword(s):

Clustering Analysis ◽

Particle Swarm ◽

Particle Swarm Algorithm ◽

Dynamic Clustering ◽

Swarm Algorithm

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Dynamic clustering analysis for driving styles identification

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2020.104096 ◽

2021 ◽

Vol 97 ◽

pp. 104096

Author(s):

Maria Valentina Niño de Zepeda ◽

Fanlin Meng ◽

Jinya Su ◽

Xiao-Jun Zeng ◽

Qian Wang

Keyword(s):

Clustering Analysis ◽

Dynamic Clustering

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Balance Dynamic Clustering Analysis and Consensus Reaching Process with Consensus Evolution Networks in Large-scale Group Decision Making

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2019.2953602 ◽

2019 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

Tong Wu ◽

Xinwang Liu ◽

Jindong Qin ◽

Francisco Herrera

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Clustering Analysis ◽

Large Scale ◽

Group Decision ◽

Dynamic Clustering ◽

Consensus Reaching Process ◽

Consensus Reaching

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Evaluation and Classification of Black Soil Structure Degradation in Jilin by Factor Analysis and Dynamic Clustering Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.301-303.472 ◽

2011 ◽

Vol 301-303 ◽

pp. 472-477 ◽

Cited By ~ 1

Author(s):

Cen Cen Niu ◽

Qing Wang ◽

Wen Zhang ◽

Ke Wang ◽

Zhen Guo

Keyword(s):

Factor Analysis ◽

Clustering Analysis ◽

Degradation Mechanism ◽

Black Soil ◽

Jilin Province ◽

Dynamic Clustering ◽

Structure Degradation ◽

Three Samples ◽

Principal Factors ◽

Matlab Programming

The black soil in Jilin Province, famous for providing significant amount of commodity grain to entire China, had already begun to regress to disappear on different degrees. To study the structure degradation of black soil in Jilin Province, We used SPSS and matlab programming on data information processing, thirty indexes were chosen, factor analysis on twenty-three samples from different sites and depths in Jilin Province was carried out, and dynamic clustering analysis taking the score of main factor as the variable was performed in turn. Combining them together, as a result, then the degradation mechanism was evaluated and classified. The results showed that eight factors from thirty indexes were found as the principal factors by factor analysis. By dynamic clustering analysis, the areas where twenty-three samples came from could be classified into three types: high degradation, medium degradation and light degradation. Finally, the differences among index scores were analyzed respectively, and reasonable suggested measures to prevent the black soil degradation were stated.

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