Abstract — Adaptive neuro fuzzy inference system (ANFIS) and fuzzy c–means (FCM) clustering algorithm is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to find the results and analysis of selected data set that we choose for ANFIS and FCM. To find out the results of the data set choosen, a few tools have been used to make the experiment run well. Hence, based on the results that we obtained, we will give the explanation according to our knowledge, understanding by done some research and running experimentation so it can be distribute to other people that wanted to know more about ANFIS and FCM with using MATLAB.
Keywords: ANFIS, FCM, experimentation, results and analysis
In the field of software, data analysis is considered as very useful and important tools as the task of processing large volume of data is rather tough and it has accelerated the interest of application of such analysis. Data clustering is primarily a method of data description which is used as a common technique for analysis in various fields like machine learning, data mining, pattern recognization, image analysis and bio-informatics. Cluster analysis also recognised as an important technique for classifying data, finding clusters of a dataset based on similarities in the same cluster and dissimilarities between different clusters.
An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of aritificial network that is based on Tagaki-Sugeno fuzzy inference system. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework 1. Its inference system corresponds to a set of fuzzy IF THEN rules that have learning capability to approximate non-linear functions 2. In using the ANFIS in more efficient and optimal way, one can use the best parameters obtained by genetic algorithm 3.
Meanwhile Fuzzy C-means (FCM) is a data clustering technique where in each data point belongs to a cluster to some degree that is specified by a membership grade. This technique was originally introduced by Jim Bezdek in 1981 as an improvement on earlier clustering methods 4. FCM is an unsupervised clustering algorithm that is applied to wide range of problems connected with feature analysis applied in argicultural engineering, astronomy, chemistry, geology, image analysis, medical diagnosis, shape analysis and target recognation.
In this project will be explain and discuss about the topic of ANFIS and FCM. There will be a one (1) example of data set for each of them. The dataset that has been choose for ANFIS is restaurant inspection and Iris for FCM. Other than that, there will be experimentation to find the decision making by using ANFIS and FCM for clustering dataset. In ANFIS and FCM, MATLAB software has been used to find the results. After finding all the results of ANFIS and FCM, the analysis will be shown and explain for each of it. Then, theirs advantages and disadvantages also will be provided. Thus, there will be an conclusion about the relationship between ANFIS and c-mean (FCM)
II. EXPERIMENTATION OF FCM
A. Iris Data Set
This data set is using two clusters which are iris setosa and the other cluster is containing both iris virginica and iris versicolor. This makes the data set a good example to explain the difference between supervised and unsupervised techniques in data mining.
B. Steps to Find FCM for Iris Data Set using MATLAB
This data set is using matlab software to do fuzzy c-mean. Most of the steps are using code.
Step 1: Load the data using command prompt shown in figure 1.