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A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data

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Abstract

Cluster extraction is a vital part of data mining; however, humans and computers perform it very differently. Humans tend to estimate, perceive or visualize clusters cognitively, while digital computers either perform an exact extraction, follow a fuzzy approach, or organize the clusters in a hierarchical tree. In real data sets, the clusters are not only of different densities, but have embedded noise and are nested, thus making their extraction more challenging. In this paper, we propose a density-based technique for extracting connected rectangular clusters that may go undetected by traditional cluster extraction techniques. The proposed technique is inspired by the human cognition approach of appropriately scaling the level of detail, by going from low level of detail, i.e., one-way clustering to high level of detail, i.e., biclustering, in the dimension of interest, as in online analytical processing. A number of experiments were performed using simulated and real data sets and comparison of the proposed technique made with four popular cluster extraction techniques (DBSCAN, CLIQUE, k-medoids and k-means) with promising results.

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Acknowledgments

This project was supported by the NSTIP strategic technologies program in the Kingdom of Saudi Arabia—Project No. (12-AGR2709-3). The authors also, acknowledge with thanks Science and Technology Unit, King Abdulaziz University for technical support.

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Authors and Affiliations

  1. King Abdulaziz University, Jeddah, Saudi Arabia

    Ahsan Abdullah

  2. University of Stirling, Stirling, Scotland, UK

    Amir Hussain

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  2. Amir Hussain
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Correspondence to Ahsan Abdullah.

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Abdullah, A., Hussain, A. A Cognitively Inspired Approach to Two-Way Cluster Extraction from One-Way Clustered Data. Cogn Comput 7, 161–182 (2015). https://doi.org/10.1007/s12559-014-9281-0

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