Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and non-churners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensib...

A fuzzy clustering algorithm for intrusion detection based on heterogeneous attributes is proposed in this paper. Firstly, the algorithm modifies the comparability measurement for the categorical attributes according to the formula of Hemingway; then, for the shortages of fuzzy Cmeans clustering algorithm: initialize sensitively and easy to get into the local optimum, the presented new algorith...

To accurately estimate the state of charge of a lithium-ion battery pack used in electric vehicles, a neurofuzzy system is proposed. The subtractive clustering is used to determine the structure and the initial parameters of the neuro-fuzzy system to reduce heuristic errors. The algorithm of adaptive neuro-fuzzy inference (ANFIS) is adopted to optimize the parameters of the neuro-fuzzy system. ...

Logika fuzzy adalah salah satu komponen yang membentuk komputasi lunak, merupakan cara mudah untuk memetakan ruang input ke output. Dalam banyak kasus, logika digunakan menyelesaikan masalah dari hingga sering hal ini Fuzzy C-Means Clustering akan dalam jurnal ini. CMeans (FCM) atau dikenal dengan ISODATA bagian metode KMeans. Derajat keberadaan data suatu kelas kelompok ditentukan oleh derajat...

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds...

An improved initialization method for fuzzy cmeans (FCM) method is proposed which aims at solving the two important issues of clustering performance affected by initial cluster centers and number of clusters. A density based approach is needed to identify the closeness of the data points and to extract cluster center. DBSCAN approach defines ε–neighborhood of a point to determine the core objec...

The most widely used clustering algorithm implementing the fuzzy philosophy is Fuzzy CMeans (FCM) .In this paper, we have proposed a new Hybrid FCM with Genetic Algorithm (GA), we get an improved FCM algorithm which has not only the global search capability of GA but also the local search capability of FCM, and hence can better solve the clustering problem. An improved version of this hybrid cl...

The presented control scheme utilizes Adaptive Neuro Fuzzy Inference System (ANFIS) controller to track rotational speed of a reference engine and disturbance rejection during engine idling. To evaluate the performance of the controller a model of the system is developed and simulation results are presented. It is shown that the ANFIS controller is suitable for control systems with large time d...

this paper presents a comparative study between three versions of adaptive neuro-fuzzy inference system (anfis) algorithms and a pseudo-forward equation (pfe) to characterize the north sea reservoir (f3 block) based on seismic data. according to the statistical studies, four attributes (energy, envelope, spectral decomposition and similarity) are known to be useful as fundamental attributes in ...

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the jo...