TY - JOUR ID - 72631 TI - A Geometric View of Similarity Measures in Data Mining JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Hassanpour, Hamid AU - Darvishi, Ali AD - Y1 - 2015 PY - 2015 VL - 28 IS - 12 SP - 1728 EP - 1737 KW - Data mining KW - Feature Extraction KW - Similarity measures KW - Geometric view DO - N2 - The main objective of data mining is to acquire information from a set of data for prospect applications using a measure. The concerning issue is that one often has to deal with large scale data. Several dimensionality reduction techniques like various feature extraction methods have been developed to resolve the issue. However, the geometric view of the applied measure, as an additional consideration, is generally neglected. Since each measure has its own perspective to the data, different interpretations may achieved on data depending on the used measure. While efforts are often focused on adjusting the feature extraction techniques for mining the data, choosing a suitable measure regarding to the nature or general characteristics of the data or application is more appropriate. Given a couple of sequences, a specific measure may consider them as similar while another one may quantify them as dissimilar. The goal of this research is twofold: to evince the role of feature extraction in data mining, and to reveal the significance of similarity measures geometric attributes in detecting the relationships between data. UR - https://www.ije.ir/article_72631.html L1 - https://www.ije.ir/article_72631_72d3d4c8d57555be359294e5d7000fd3.pdf ER -