Performance Comparison of Rule Generation Method Substractive Clustering and Fuzzy C-Means Clustering on Sugeno's Inference for Stroke Risk Detection

Abstract

<span class="fontstyle0">Abstract - Fuzzy Inference is one method that can<br />solve the problem of uncertainty in a decision-making<br />or classification well. In inference, fuzzy rules that<br />represent the need of expert knowledge in the relevant<br />fields, so that the classification given decision or be<br />appropriate expert knowledge. However there are times<br />when experts are less able to represent the rules of the<br />appropriate knowledge or knowledge that there is need<br />of too many rules, so we need a method that can<br />generate rules based on the data given expert.<br />At issue troke s disease risk detection, it also occurs<br />because of the research that has been done by taking the<br />direct rule of experts, it turns out less than the maximum<br />accuracy, still 82.89%. Substractive methods<br />Clustering and Fuzzy C-Means (FCM) could generate<br />rules by grouping algorithm, in which the existing<br />training data are grouped in common and the rules of<br />the group raised. Differences in the two methods are in<br />determining the center of the cluster and assign each<br />incoming data which groups.<br />Based on research that has been done, substractive<br />average Clustering membrika better accuracy is<br />84.46%, while 73.81% FCM. However, in the<br />processing time FCM faster at 16.75 seconds to give an<br />average processing time of 13:02 seconds.</span>