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

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.