sis 20(24): e3

Research Article

Heterogenetic knowledge classification Using Fuzzy inference for unified data clusters

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  • @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
Umer Farooq1,2,*, Khalil Ahmad1,3
  • 1: National College of Business Administration & Economics, Lahore, Pakistan
  • 2: Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
  • 3: Delta3T, Lahore, Pakistan
*Contact email: umerfarooq@lgu.edu.pk

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.