DGLC: a density-based global logical combinatorial clustering algorithm for large mixed incomplete data

Clustering has been widely used in areas as pattern recognition, data analysis and image processing. Recently, clustering algorithms have been recognized as one of a powerful tool for data mining. However, the well-known clustering algorithms offer no solution to the case of large mixed incomplete data sets. The authors comment the possibilities of application of the methods, techniques and philosophy of the logical combinatorial approach for clustering in these kinds of data sets. They present the new clustering algorithm DGLC for discovering β₀ -density connected components from large mixed incomplete data sets. This algorithm combines the ideas of logical combinatorial pattern recognition with the density based notion of cluster. Finally, an example is showed in order to illustrate the work of the algorithm