K-means clusteringIn this paper, a novel network called K-Means clustering based Extreme Learning ANFIS (KMELANFIS) with improved interpretability for regression problems is presented. Grid input space partitioning results in the exponential rise in the number of rules in Fuzzy Inference System (FIS...
K-ESNNK-meansSpiking Neural NetworkIn this paper, a novel K-means evolving spiking neural network (K-ESNN) model for clustering problems has been presented. K-means has been utilised to improve the original ESNN model. This model enhances the flexibility of the ESNN algorithm in producing ...
Numerous research efforts on data clustering have been offered throughout the past decades. To cluster a dataset, there are various solutions to the clustering problem. These methods primarily use complicated network approaches, \(K\)-means, and its improved variants, metaheuristic algorithms, and ot...
K-means Clustering Traveling Salesman Problem, and Graph Coloring These algorithms have a property similar to ones in –they can all be reduced to any problem in . Because of that, these are in and are at least as hard as any other problem in . A problem can be both in and , which ...
Aiming at the fault line selection problem in the single-phase grounding system of the distribution network, a new fault line selection method based on VMD and permutation entropy feature extraction combined with K-means clustering algorithm is proposed. This method is a hybrid algorithm that can ...
In this case, the K-means clustering algorithm is independently applied to minority and majority class instances. This is to identify clusters in the dataset. Subsequently, each cluster is oversampled such that all clusters of the same class have an equal number of instances and all classes have...
In this case, the K-means clustering algorithm is independently applied to minority and majority class instances. This is to identify clusters in the dataset. Subsequently, each cluster is oversampled such that all clusters of the same class have an equal number of instances and all classes have...
Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be appro
Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City 2022, Sensors Comparative Assessment of Digital and Conventional Soil Mapping: A Case Study of the Southern Cis-Ural Region, Russia 2022, Soil Systems The Systems...
After that Sulaiman and Isa [11] introduced a new segmentation method based on a new clustering algorithm which is Adaptive Fuzzy k-means Clustering Algorithm (AFKM). On the other hand, the Fuzzy C-Means (FCM) algorithm was used in image segmentation but it still has some drawbacks that ...