A Computational Cost-Effective Clustering Algorithm in Multidimensional Space Using the Manhattan Metric: Application to the Global Terrorism Database
The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130685Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
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