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Discovery of Customer Communities – Evaluation Aspects

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Information Technology for Management: Current Research and Future Directions (AITM 2019, ISM 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 380))

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Abstract

In the paper, a new multi-level hybrid method of community detection combining a density-based clustering with a label propagation method is evaluated and compared with the k-means benchmark and DBSCAN (Density-based spatial clustering of applications with noise). In spite of the sophisticated visualization methods, managers still usually find clustering results too difficult to evaluate and interpret. The article presents a set of key assessment measures that could be used to evaluate internal and external qualities of discovered clusters. The approach is validated on real life marketing database using advanced analytics platform, Upsaily.

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Notes

  1. 1.

    Between-Cluster Dispersion can be calculated as \( BCD\left( n \right) = \mathop \sum \nolimits_{i} \overline{{c_{i} }} \cdot d^{2} (c_{i} ,c) \), where \( n \) is the number of clusters, \( d\left( {c_{i} , c} \right) \) is the distance between centroid of the cluster \( c_{i} \) and the global center of all clusters, \( \overline{{c_{i} }} \) is the number of elements in the cluster \( c_{i} \).

  2. 2.

    Within-Cluster Dispersion can be calculated as \( WCD\left( n \right) = \mathop \sum \nolimits_{i} \mathop \sum \nolimits_{{x \in c_{i} }} d^{2} \left( {x, c_{i} } \right) \), where \( n \) is the number of clusters, \( x \) is an element of the cluster \( c_{i} \), \( d\left( {x, c_{i} } \right) \) is the distance between centroid of the cluster \( c_{i} \) and the element \( x \) belonging to the cluster \( c_{i} \).

  3. 3.

    The code for Dunn index calculation was found on GitHub: Dunn index for clusters analysis - https://gist.github.com/douglasrizzo/cd7e792ff3a2dcaf27f6. Computing of Dunn index is relatively simple, authors verified published code before it was used in order to prove its validity.

  4. 4.

    Implementation of Davies–Bouldin index was taken from Scikit learn library: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.davies_bouldin_score.html.

  5. 5.

    Implementation of Silhouette index was taken from Scikit learn library: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html.

  6. 6.

    Implementation of Calinski-Harabasz index was taken from Scikit learn library: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.calinski_harabasz_score.html.

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Author information

Authors and Affiliations

  1. International University of Logistics and Transport, ul. Sołtysowicka 19B, 51-168, Wrocław, Poland

    Jerzy Korczak

  2. Wroclaw University of Economics and Business, ul. Komandorska 118-120, 53-345, Wrocław, Poland

    Maciej Pondel & Wiktor Sroka

Authors
  1. Jerzy Korczak
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  2. Maciej Pondel
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  3. Wiktor Sroka
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Corresponding authors

Correspondence to Jerzy Korczak , Maciej Pondel or Wiktor Sroka .

Editor information

Editors and Affiliations

  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

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Korczak, J., Pondel, M., Sroka, W. (2020). Discovery of Customer Communities – Evaluation Aspects. In: Ziemba, E. (eds) Information Technology for Management: Current Research and Future Directions. AITM ISM 2019 2019. Lecture Notes in Business Information Processing, vol 380. Springer, Cham. https://doi.org/10.1007/978-3-030-43353-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-43353-6_10

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