@article{MAKHILLJEAS2019141217957,
    title = {Fuzzy Inference System Model from Non-Fuzzy Clustering Output},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {12},
    pages = {4035-4042},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.4035.4042},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.4035.4042},
    author = {Nur Atiqah Binti and},
    keywords = {Household income data,fuzzy inference system,k-means clustering,root mean square,prediction model,Root Mean Square Error (RMSE)},
    abstract = {Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic
theory where decisions can be made or patterns are discerned. This study aims to discuss on how non-fuzzy
clustering output can be used to construct a model of FIS. Here, the proposed idea is to show the efficient use
of the FIS as a prediction model for the data classification. In this study, employment income, self-employment
income, property and transfer received are taken into account for clustering the household income data. Then,
the FIS prediction model is built using the center values of clusters formed and the output of FIS is compared
to the original cluster in which the best fit prediction model to the data is determined. In conclusion, the best
prediction model in identifying income class is discovered based on the Root Mean Square Error (RMSE) value
computed.}
    }