A Weighted Mean Subtractive Clustering Algorithm

TL;DR: In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based aquaphotomics approach is conducted on the study of water quality monitoring, assessment, and analysis in an aquaponics system are vital procedures in maintaining a productive and efficient ecosystem for cultivars being cultured.

TL;DR: A dynamic multi-agent system using particle swarm optimization for the e-learning systems is proposed and demonstrates the effectiveness of the proposed system in providing near-optimal solutions in considerably less computational time.

TL;DR: A new approach to subtractive clustering algorithm (SC) with the fuzzifier parameter m which controls the clustering results in SC is introduced and the SC algorithm is expanded to interval type-2 fuzzy subtractives clustering algorithms (IT2-SC) using two fuzzifiers parameters m 1 and m 2 which creates a footprint of uncertainty (FOU) for the fuzzifiers.

TL;DR: In this article , the prediction of power peaking factors (PPF) using the ANFIS was conducted, and the results showed that the model could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

TL;DR: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster which can be used to infer the appropriateness of data partitions.

TL;DR: An efficient method for estimating cluster centers of numerical data that can be used to determine the number of clusters and their initial values for initializing iterative optimization-based clustering algorithms such as fuzzy C-means is presented.

TL;DR: This work reviews two clustering algorithms and three indexes of crisp cluster validity and shows that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters.

TL;DR: A simple and effective approach for approximate estimation of the cluster centers on the basis of the concept of a mountain function, based upon a griding on the space, the construction of amountain function from the data and then a destruction of the mountains to obtain the cluster center centers.