K-means

Open Access Article

1 - A non-disruptive multi-objective charging strategy for WRSN through multi-UAV deployment optimization using a meta-heuristic algorithm
Payman Habibi Goran Hassanifard Abdulbaghi Ghaderzadeh Arez Nosratpour

Here, a planning approach for CUAVs movement path and charging schedule of sensor nodes under uncertainty in data transfer rate and energy consumption in nodes with the help of Harris Hawks Optimization (HHO) and gradient-based optimization (GBO) algorithms have been pr More

Here, a planning approach for CUAVs movement path and charging schedule of sensor nodes under uncertainty in data transfer rate and energy consumption in nodes with the help of Harris Hawks Optimization (HHO) and gradient-based optimization (GBO) algorithms have been presented. By considering the inequalities and uncertainty in the battery limit and energy consumption of the nodes, we will achieve new scheduling strategies for WRSNs to increase the charging throughput and increase the network lifetime. Initially, with the help of information about the position and remaining energy of the nodes, clustering of the nodes into the number of drones has been done by the K-means method. According to the definition of the multi-purpose function of CUAV and with the help of the proposed algorithms, the routing and charging schedule of each of the drones is planned. In the defined objective function, all uncertainties and inequalities of the network are included for the delay and consumption of energy and battery of the nodes. The simulation was done under MATLAB software. The results showed that the proposed method based on HHO has achieved better solutions in terms of increasing the network lifetime and reducing the delay and optimizing energy consumption. Manuscript profile

Open Access Article

2 - Clustering with Intelligent Linexk-Means
نرگس Ahmadzadehgolia M.H. Behzadi A. Mohammadpour

The intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the c More

The intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the centers in each cluster is not randomly. Choosing the LINEX dissimilarity measure helps the researcher to overestimate or underestimate the centers which helps to assign some entities into a special cluster. We check the performance of the algorithm on some real and artificial datasets and evaluate the results according to some internal and external indexes. Manuscript profile

Open Access Article

3 - Determination of homogenous areas for ecosystem services supply in the central part of Isfahan province
Sedighe Abdollahi Alireza Ildoromi Abdolrassoul Salmanmahini Sima Fakheran

10.30495/girs.2020.672371

Determining and identifying homogeneous regions for ecosystem services supply is an effective and useful step in improving land management. Therefore, in this study, after quantifying and mapping ecosystem services, aesthetic value, recreational value, and noise polluti More

Determining and identifying homogeneous regions for ecosystem services supply is an effective and useful step in improving land management. Therefore, in this study, after quantifying and mapping ecosystem services, aesthetic value, recreational value, and noise pollution reduction, the K-Means clustering method was used to identify homogeneous areas of ecosystem service supply and homogeneous areas zoning was prepared in the GIS environment. To investigate the effective parameters on ecosystem services supply, the slope, altitude, population density, distance from access routes, distance from the river, percentage of available land uses and distance from the centre of the largest urban region were extracted for each homogeneous area or cluster. Based on the Davis-Bouldin validation index, the optimal number of clusters was 4. Cluster number two with the area of 686.27 Km2 was the largest, while cluster number one with the area of 119.75 Km2 was the smallest in the area. Investigation of environmental-social parameters showed that land use has the highest impact on ecosystem services supply. The results showed that there is a direct relationship between these parameters and ecosystem services supply in each cluster. Based on the results of this study, investigation of homogeneous areas of ecosystem services can be effective to improve land use planning and management. Manuscript profile

Open Access Article

4 - A new algorithm for data clustering using combination of genetic and Fireflies algorithms
Mahsa Afsardeir mansoure Afsardeir

Introduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret More

Introduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret data by exploring the structures using similarities or differences. One of the most widely used clustering methods is the k-means. In this algorithm, cluster centers are randomly selected and each object is assigned to a cluster that has maximum similarity to the center of that cluster. Therefore, this algorithm is not suitable for outlier data since this data easily changes centers and may produce undesirable results. Therefore, by using optimization methods to find the best cluster centers, the performance of this algorithm can be significantly improved. The idea of combining firefly and genetics algorithms to optimize clustering accuracy is an innovation that has not been used before.Method: In order to optimize k-means clustering, in this paper, the combined method of genetic algorithm and firefly worm is introduced as the firefly genetic algorithm.Findings: The proposed algorithm is evaluated using three well-known datasets, namely, Breast Cancer, Iris, and Glass. It is clear from the results that the proposed algorithm provides better results in all three datasets. The results confirm that the distance between clusters is much less than the compared approaches.Discussion and Conclusion: The most important issue in clustering is to correctly determine the cluster centers. There are a variety of methods and algorithms that performs clustering with different performance. In this paper, based on firefly metaheuristic algorithms and genetic algorithms a new method has been proposed for data clustering. Our main focus in this study was on two determining factors, namely the distance within the data cluster (distance of each data to the center of the cluster) and the distance that the headers have from each other (maximum distance between the centers of the clusters). In the k-means algorithm, clustering is not accurate since the cluster centers are selected randomly. Employing firefly algorithms and genetics, we try to obtain more accurate centers of the clusters and, as a result, correct clustering. Manuscript profile

Open Access Article

5 - An algorithm for clustering of insurance products and users in a collaborative filtering-based insurance recommender system and evaluating its performance based on the insurance recommendation
Marzieh Amini Shirkoohi Mohammadreza Yamaghani

Introduction There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance be More

Introduction There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance because of various type of insurance and lack of enough information of insurance service. Choosing the proper insurance service always related to people personal and social features Method Prediction of customer’s insurance selection according to people personal and social property especially thier financial condition play vital role. On one hand Prediction of insurance type can help people who want to utilize insurance service. On the other hand this prediction can facilitate process of insurance for Insurers too. There are multiple important mechanisms and factors like customers clustring, analyze each class feature, detection of popular insurance in each class and using Collaborative filtering technique to offer best insurance that can influence on process of decision and selection the suitable insurance. Results The total precision value of the proposed method is 89.98% for joint insurances of similar users. Also, the total value of the F-measure of the proposed method for joint insurances between similar customers is 87.13%. Discussion Customer behavior can be predicted by available data of people’s personal and social features and type of insurance that they are chosen and rate of their satisfactions. K-means clustring algorithm and recommender systems Techniques like Collaborative filtering are two significant mechanisms to implement prediction of customer’s behaviors. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Manuscript profile

Open Access Article