Partitioning and hierarchical based clustering: a comparative empirical assessment on internal and external indices, accuracy, and time


Clustering is an unsupervised data mining technique where exploration is done with little knowledge of data classes. Its aim is to recognize the hidden information from the data for effective decision-making. Though many clustering algorithms has already been implemented till date, still it is an active topic of research for data mining. Researcher’s attempts to explore, compare, evaluate, and improve the different clustering algorithms available, for specialized situation and context. The purpose of all these efforts are to refine and propose improved version of algorithm after statistical evaluation by different metrices. The present research is an attempt to analysis empirically, the partitioning based clustering algorithms and hierarchical based clustering algorithm; by conducting extensive experiments. Both algorithms effectiveness has been measured through external and internal validity indices and Pearson’s correlation distance function using anatomized experiments. The parameters of evaluation that have been taken into consideration; for Internal Indices: Silhouette Index, Davies-Bouldin Validity Index and Calinski-Harabasz index; for external indices: Jaccard index, Rand Index, Entropy and Normalized Mutual Information. The other parameters of evaluation are accuracy and time of execution. Based on the experiments it may be concluded that K-means algorithm produces more promising result than hierarchical algorithm except in accuracy.

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Correspondence to Syed Imtiyaz Hassan.

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Hassan, S.I., Samad, A., Ahmad, O. et al. Partitioning and hierarchical based clustering: a comparative empirical assessment on internal and external indices, accuracy, and time. Int. j. inf. tecnol. 12, 1377–1384 (2020).

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  • Data mining
  • Data science
  • Machine learning
  • Clustering algorithm
  • K-means
  • Hierarchical algorithm
  • Validation indices
  • Pearson’s correlation distance