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Average Linkage Hierarchical Clustering Algorithm for Determining the Relationships between Elements in Coal.

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This summary is machine-generated.

The average linkage hierarchical clustering algorithm offers superior insights into element occurrence in coal compared to older methods. This statistical approach aids in understanding coal

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Area of Science:

  • Geochemistry
  • Environmental Science
  • Data Mining

Background:

  • Element occurrence in coal impacts environmental and human health.
  • Statistical methods, particularly hierarchical clustering, are used to determine element occurrence.
  • Different clustering algorithms can yield conflicting results, necessitating performance evaluation.

Purpose of the Study:

  • To evaluate the effectiveness of different hierarchical clustering algorithms for determining element occurrence in coal.
  • To compare the average linkage algorithm with the centroid linkage algorithm using Adaohai coal data.

Main Methods:

  • Hierarchical clustering algorithms (average linkage and centroid linkage).
  • Correlation similarity and Pearson correlation similarity measures.
  • Analysis of elemental data from Adaohai coal samples.

Main Results:

  • The average linkage algorithm with correlation similarity provided more effective analysis of geological processes.
  • Surprising insights were observed for selenium, beryllium, and thallium, challenging existing geochemical principles.
  • The average linkage method proved more robust than the centroid linkage method for this dataset.

Conclusions:

  • The average linkage hierarchical clustering algorithm with correlation similarity is highly effective for analyzing geological processes in coal.
  • This method offers improved accuracy and clarity in determining element modes of occurrence compared to previous statistical approaches.
  • Further investigation into element occurrence using advanced statistical methods is warranted.