A brief survey of unsupervised agglomerative hierarchical clustering schemes

 
 
 
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  • References
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  • Abstract


    Unsupervised hierarchical clustering process is a mathematical model or exploratory tool aims to provide the easiest way to categorize the distinct groups over the large volume of real time observations or dataset in tree form based on nature of similarity measures without prior knowledge. Dataset is an important aspect in the hierarchical clustering process that denotes the behavior of living species depicts the properties of a natural phenomenon and result of a scientific experiment and observation of a running machinery system without label identification. The hierarchical clustering scheme consists of Agglomerative and Divisive that is applicable to employ into various scientific research areas like machine learning, pattern recognition, big data analysis, image pixel classification, information retrieval, and bioinformatics for distinct patterns identification. This paper discovered a brief survey of agglomerative hierarchical clustering schemes with its clustering procedures, linkage metrics, complexity analysis, key issues and development of AHC scheme.

     


  • Keywords


    Agglomerative Hierarchical Clustering; Clustering Process; Distance Metric; Divisive Hierarchical Clustering; Similarity Measure; Linkage Method.

  • References


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Article ID: 13971
 
DOI: 10.14419/ijet.v8i1.13971




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