Data mining misnomer nomenclature: myth or myopic based on its evolutional and trend analysis

Abstract

Data mining (DM) has tremendous advantages for analysing largescale data for different fields. However, it has also a remarkable naming or nomenclature problem. It lacks a standard definition, which needs to be consistent for researchers regardless of their research capability. Because of its loose definition, it means an exploration of massive data as different things to a different audience. If so, is it a myth or myopic nomenclature of DM misnomer? Therefore, in this study, we investigated the naming seductiveness, which gives a novel idea on how and why researchers need to be concerned of their new findings or artefacts' proper naming. What motivated the authors to undertake a deep investigation of the unleashed power of a sedulous naming to gain a clear insight and knowing the advantages of proper and standard naming for the final annotations is an interesting issue. The approach proofed by empirical analysis as the DM trends for future prospects.