Research Article
Heterogenetic knowledge classification Using Fuzzy inference for unified data clusters
@ARTICLE{10.4108/eai.13-7-2018.160072, author={Umer Farooq and Khalil Ahmad}, title={Heterogenetic knowledge classification Using Fuzzy inference for unified data clusters}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={7}, number={24}, publisher={EAI}, journal_a={SIS}, year={2019}, month={9}, keywords={GPS, IoT, FIS, Knowledge Heterogeneity, Knowledge System}, doi={10.4108/eai.13-7-2018.160072} }- Umer Farooq
Khalil Ahmad
Year: 2019
Heterogenetic knowledge classification Using Fuzzy inference for unified data clusters
SIS
EAI
DOI: 10.4108/eai.13-7-2018.160072
Abstract
Emerging technologies such as Cloud Computing, Internet of Things (IoT) and Big Data are developing a digital ecosystem. This ecosystem is catering diverse types and volumes of data that represents information segments. The essence of these segments become vital when transformed into knowledge units to provide a more meaningful and productive perspective. The transformed knowledge at this stage is heterogenetic in nature, consisting of functional and structural properties which needs to be arranged to formulate robust and efficient knowledge repositories. The heterogenetic knowledge can be transformed into classification clusters using structural properties by controlling the degree of heterogeneity. In this paper, Fuzzy Inference System (FIS) based classification approach is proposed for heterogenetic knowledge clustering.
Copyright © 2019 Umer Farooq et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
