Unsupervised clustering of materials properties using hierarchical techniques

Data mining (DM) algorithms arose as a promising and flourishing discipline at manufacturing and industrial engineering. This paper proposes an efficient decision support approach for manufacturing engineering. The proposed approach tackles clustering challenges for engineering materials properties. It adopts the hierarchal clustering for mining engineering materials properties. Extensive experiments and comparisons are conducted on three different real-world datasets for engineering materials properties. In addition, a study of different similarity measures is carried out to choose the best fit similarity measure to engineering material datasets. A comparison of the results with other competitors clearly shows the robustness of the proposed approach. Therefore, it is highly recommended to use the proposed approach as a scalable engineering material properties tool.