A New QoS Prediction Model using Hybrid IOWA- ANFIS with Fuzzy C-Means, Subtractive Clustering and Grid Partitioning

In this study, clustering data on STMIK STIKOM Indonesia alumni using the Fuzzy C-Means and Fuzzy Subtractive methods. The method used to test the validity of the cluster is the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index. Clustering is carried out with the aim of finding hidden patterns or information from a fairly large data set, considering that so far the alumni data at STMIK STIKOM Indonesia have not undergone a data mining process. The results of measuring cluster validity using the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index, the Fuzzy C-Means Clustering algorithm has a higher level of validity than the Fuzzy Subtractive Clustering algorithm so it can be said that the Fuzzy C-Means algorithm performs the cluster process better than with the Fuzzy Subtractive method in clustering alumni data. The number of clusters that have the best fitness value / the most optimal number of clusters based on the CE and MPC validity index is 5 clusters. The cluster that has the best characteristics is the 1st cluster which has 514 members (36.82% of the total alumni). With the characteristics of having an average GPA of 3.3617, the average study period is 7.8102 semesters and an average TA work period of 4.9596 months.

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Comparative ANFIS Models for Stochastic On-road Vehicle CO2 Emission using Grid Partitioning, Subtractive, and Fuzzy C-means Clustering

10.1109/r10-htc53172.2021.9641644 ◽

2021 ◽

Author(s):

Maria Gemel B. Palconit ◽

Ronnie S. Conception ◽

Jonnel D. Alejandrino ◽

Warren A. Nunez ◽

Argel A. Bandala ◽

...

Keyword(s):

Co2 Emission ◽

Road Vehicle ◽

Fuzzy C Means ◽

Grid Partitioning ◽

Fuzzy C Means Clustering

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Fuzzy subtractive clustering based prediction model for brand association analysis

MATEC Web of Conferences ◽

10.1051/matecconf/201815401082 ◽

2018 ◽

Vol 154 ◽

pp. 01082

Author(s):

Imam Djati Widodo

Keyword(s):

Image Quality ◽

Prediction Model ◽

Association Analysis ◽

Fuzzy Clustering ◽

Subtractive Clustering ◽

Brand Performance ◽

Product Attributes ◽

Accuracy Rate ◽

Brand Association

The brand is one of the crucial elements that determine the success of a product. Consumers in determining the choice of a product will always consider product attributes (such as features, shape, and color), however consumers are also considering the brand. Brand will guide someone to associate a product with specific attributes and qualities. This study was designed to identify the product attributes and predict brand performance with those attributes. A survey was run to obtain the attributes affecting the brand. Subtractive Fuzzy Clustering was used to classify and predict product brand association based aspects of the product under investigation. The result indicates that the five attributes namely shape, ease, image, quality and price can be used to classify and predict the brand. Training step gives best FSC model with radii (ra) = 0.1. It develops 70 clusters/rules with MSE (Training) is 9.7093e-016. By using 14 data testing, the model can predict brand very well (close to the target) with MSE is 0.6005 and its’ accuracy rate is 71%.

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Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Symmetry ◽

10.3390/sym10120700 ◽

2018 ◽

Vol 10 (12) ◽

pp. 700 ◽

Cited By ~ 7

Author(s):

Chan-Uk Yeom ◽

Keun-Chang Kwak

Keyword(s):

Performance Comparison ◽

Combined Cycle ◽

Subtractive Clustering ◽

Segmentation Method ◽

Clustering Method ◽

Space Partitioning ◽

Anfis Model ◽

Inference System ◽

Input Space ◽

Fuzzy C Means

In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.

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