fuzzy clustering

Recommender systems are a subset of intelligent information filtering systems that discovers user interests and provide user-friendly recommendations. User-based collaborative filtering recommender systems is one of the most important types of recommender systems. However, they are faced with voluminous data and sparsity problems that have negative effects on the performance of the systems. In the proposed method, fuzzy C-ordered means clustering algorithm is integrated with a chaotic self-adaptive particle swarm evolutionary algorithm for clustering users. The proposed method aims to improve the rating prediction in large sparse datasets and reduce the negative impact of outliers and noisy data. Experiments have been conducted on real-world datasets to evaluate and prove the efficiency of the proposed method. Experimental results show the superiority of the proposed method that the state-of-the-art methods based on prediction error criteria, accuracy rates, and the computational time.

Detection of calcium particles in breast mammography images is important in the early detection of cancer. Identification of these particles is done manually by experts, which is associated with high cost and error. In this paper, a new method based on fuzzy clustering algorithm for fine-grained detection in mammographic images is proposed. In the proposed method, the low quality of mammographic images is improved with the help of preprocessing. By defining an appropriate membership function in fuzzy clustering, fine-grained regions are identified. Finally, the identified areas were classified into benign and malignant groups with the help of forward propagation neural network with error propagation training algorithm. The accuracy of identification of the desired area is 96.79% and the sensitivity of this identification is 97.20%. Compared to the previous method, the accuracy and sensitivity of fine-grained identification has been improved (95% of the desired area identification accuracy and 90.52% sensitivity).  In the classification of areas with the help of neural network, the accuracy was 97.5%. Evaluation criteria showed the superiority of the proposed method in the extraction of calcium particles and classification. The reason for the superiority of the proposed method is the high accuracy in extracting the desired area as well as the distinctive features extracted from the desired area.

In this paper, a fuzzy rule extraction system is designed and the idea of ​​using this method in designing a fuzzy rule database is considered. First, the fuzzy backup vector machine is designed based on the clustering method. In this case, the backup vector machine will perform better in noise data or out-of-range data, and as a result, the rules extracted from it seem to be more accurate. The fuzzy backup vector machine model is simulated and then its backup vectors are used to derive fuzzy rules. The extracted rules are fuzzy rules that are obtained in the form of "if-then". The rules are extracted using two models of simple support vector machine and fuzzy support vector machine, which will show that the rules extracted from fuzzy backup vector machine in most cases have better accuracy in classification than ordinary backup vector machine. The number of extracted rules is an important parameter in the accuracy of the operation of this database, the number of rules extracted in the fuzzy method is less in most cases.

Wireless sensor networks are one of the most important tools for information acquisition and environment identification in many application areas. Recent advances in the field of electronics and wireless telecommunications have led to the design and manufacture of sensors with low power consumption, small size, reasonable price and various applications. Most research in the area of wireless sensor networks has focused on two dimensional sensor networks while in the real world, most of the applications are three dimensional. Some research in this area has focused on underwater, space, forestry, and environment applications. The main objective of the current research is increasing network lifetime by defining new parameters and embedding them in the fuzzy clustering or fuzzy C-means algorithm that has been adapted for three dimensional wireless sensor networks. One of the parameters that has been used in this research is limited movement of sensors. By adding this ability to the network, there is an expectation of improvement in network performance. The results of the experiments indicate the positive effect of this ability on network performance and lifetime.

Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Moreover, due to the large number of features in microarrays, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Hence, appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, a modified multi-objective cuckoo search algorithm is used to feature selection and sample selection to find the best available solutions. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. The proposed algorithm is applied on six cancer datasets and its results are compared with other existing methods. The results show that the proposed method has higher accuracy and validity in comparison to other existing approaches and is able to select the small subset of informative genes in order to increase the classification accuracy.

In this paper, an effective method for regression is presented in which a variety of fuzzy clustering methods and concepts of Hesitant fuzzy sets are used. First, the fuzzy clustering algorithm is applied to the data, and after projecting the cluster membership function on different features, the number of clusters of fuzzy sets is obtained on each dimension (or feature). We then consider these fuzzy sets as a hesitant fuzzy set on each feature, and we obtain the Hesitant Fuzzy Correlation Coefficient Matrix (HFCCM) for the attributes. Subsequently, a nonlinear mapping based on the principal components analysis of the HFCCM is used to convert the dataset's features into new features. Finally, the new extracted features are assigned to the fuzzy clustering algorithm and a Sugeno fuzzy regression system is fitted. The proposed method was compared with some other methods to several regression datasets. The results of the experiments indicate that the proposed method is successful in extracting and reducing the characteristics, as well as increasing the regression accuracy. Also, the number of rules of the fuzzy regression model in the proposed method is fairly low.

Hyperspectral images clustering, for the appropriate number of clusters, due to their high volume and high noise level is a difficult matter. The proposed method in this paper is a composition of fuzzy clustering algorithms that offers the optimal number of clusters along with the clustered image. The method is based on a hierarchical approach and acts using three fuzzy algorithms FCM, weighted G-K and MCV. Hence, the different shapes of clusters are identified and the sum volume of every cluster is minimum. Each of the clusters according to the proposed condition are separated as step to step, until the appropriate number of clusters are obtained. After implementing the proposed approach on two different hyperspectral images and use of cluster validity parameters, more appropriate values of these parameters is obtained for this method than other fuzzy methods. Finally, a good clustering can be obtained by determining the number of clusters. The results of the cluster assessment such as partition coefficient show that the proposed method, less scattered examples is presented in the clustered image, on the other hand, similarity the samples in each cluster is considerable than other methods.

Expansion planning of the electric power systems has significant importance considering increases in electricity load demand. Transmission network expansion planning (TNEP) is an important part of the electric power system development. In this paper، the TNEP is investigated as a new framework considering actual worth of network adequacy. To do so، a new economic based definition is introduced for network adequacy and statistical studies are developed to calculate the actual worth of network adequacy via proposed formulation. Next، the TNEP is investigated as an optimization problem، with two objectives، using Artificial Bee Colony algorithm. Moreover، new heuristic approaches are presented to enhance the optimization process. Detailed numerical studies and comparisons presented in the paper show that the proposed approach could improve the quality of problem solutions and can be used as a new framework for TNEP within actual networks.

This paper presents a new solution approach to solve the sub-transmission substation expansion planning (SSEP) within large distribution networks. To do so, the SSEP is investigated based on a probability matrix which contains geographical and electrical features of the studied distribution network. The developed algorithm includes different electrical constraints and considers all prevalent cost indices. Possibility of supplying any of loads from each substation is calculated through the proposed method using membership matrix, modifying center of gravity theory and providing other heuristic approaches. Complexity of the problem is substantially reduced by means of the calculated probability matrix, and optimization process is carried out based on the introduced probability matrix.Detailed numerical results and comparisons presented in the paper show that the proposed approach could noticeably improve the quality of problem solutions and can be used as an effective tool for SSEP within actual distribution networks.