clustering provinces in iran based on digital divide metric using the k-means algorithm

Authors

احمد یوسفان

ahmad yoosofan university of kashanدانشگاه کاشان الهام یوسفیان

elham yousofian

abstract

in this paper, the notion of the digital divide has been described, and a few analyzing methods of digital divide have been reviewed. analyzing methods of digital divide are called indices which have different indicators and different formulas for calculation. since data collection for an indicator may be difficult, calculating an index is an essential problem. we collected and calculated some indicators in provinces of iran. but they were insufficient to calculate a standard index. these indicators terribly show the deep digital divide between the provinces. to show more accurately the social inequalities in the adoption of ict between provinces in iran, we used the well-known k-means clustering algorithm on the indicators of the provinces. the clustering results appropriately showed the unique status of tehran among provinces because tehran always falls in a different cluster alone. it means that the information technology does not fairly spread through the provinces in irān.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...

full text

persistent k-means: stable data clustering algorithm based on k-means algorithm

identifying clusters or clustering is an important aspect of data analysis. it is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. it is a main task of exploratory data mining, and a common technique for statistical data analysis this paper proposed an improved version of k-means algorithm, namely persistent k...

full text

Enhanced Clustering Based on K-means Clustering Algorithm and Proposed Genetic Algorithm with K-means Clustering

-In this paper targeted a variety of techniques, tactics and distinctive areas of the studies that are useful and marked because the crucial discipline of information mining technologies. The overall purpose of the system of statistics mining is to extract beneficial facts from a large set of information and changing it right into a shape that is comprehensible for in addition use. Clustering i...

full text

Ranking and Clustering Iranian Provinces Based on COVID-19 Spread: K-Means Cluster Analysis

Introduction: The Coronavirus has crossed geographical borders. This study was performed to rank and cluster Iranian provinces based on coronavirus disease (COVID-19) recorded cases from February 19 to March 22, 2020. Materials and Methods: This cross-sectional study was conducted in 31 provinces of Iran using the daily number of confirmed cases. Cumulative Frequency (CF) and Adjusted CF (ACF)...

full text

A Novel K means Clustering Algorithm for Large Datasets Based on Divide and Conquer Technique

In this paper we propose an efficient algorithm that is based on divide and conquers technique for clustering the large datasets. In our research work we have applied divide and conquer technique on partitions of the large datasets and we have used squared Euclidean distance for measuring the similarity between data points. The partitioning of datasets is done according to the number of cluster...

full text

Improved K-means Clustering Algorithm Based on Genetic Algorithm

Through comparison and analysis of clustering algorithms, this paper presents an improved Kmeans clustering algorithm. Using genetic algorithm to select the initial cluster centers, using Z-score to standardize data, and take a new method to evaluate cluster centers, all this reduce the affect of isolated points, and improve the accuracy of clustering. Experiments show that the algorithm to fin...

full text

My Resources

Save resource for easier access later


Journal title:
محاسبات نرم

جلد ۱، شماره ۱، صفحات ۳۲-۴۵

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023