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

Authors: Gebeyehu Belay Gebremeskel; Birhanu Hailu; Belete Biazen

Addresses: Computing Faculty Institute of Technology, Bahir Dar University, Postcode 37, Poly Campus, Agri Building 71, Bahir Dar, Ethiopia ' Computing Faculty Institute of Technology, Bahir Dar University, Postcode 37, Poly Campus, Agri Building 71, Bahir Dar, Ethiopia ' Computing Faculty Institute of Technology, Bahir Dar University, Postcode 37, Poly Campus, Agri Building 71, Bahir Dar, Ethiopia

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.

Keywords: data mining; nomenclature; evolution; applications; trend analysis; mining techniques; predictions.

DOI: 10.1504/IJKEDM.2019.102486

International Journal of Knowledge Engineering and Data Mining, 2019 Vol.6 No.3, pp.234 - 272

Received: 10 Sep 2018
Accepted: 02 May 2019

Published online: 27 Sep 2019 *

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