Next Article in Journal
A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran
Previous Article in Journal
Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2019, 8(2), 98; https://doi.org/10.3390/ijgi8020098

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

1
Department of Information Engineering, China University of Geosciences, Wuhan 430074, China
2
National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 14 December 2018 / Revised: 6 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
Full-Text   |   PDF [5073 KB, uploaded 23 February 2019]   |  

Abstract

Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper exploration into the similarity approach mechanism for clustering polygons, thereby limiting the development of spatial clustering. In this study, we propose a novel fuzzy similarity approach for spatial clustering, called Extend Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (EIFS-IBA). When discovering polygon clustering patterns by spatial clustering, this method expresses the similarities between polygons and adjacent graph models. Shape-, orientation-, and size-related properties of a single polygon are first extracted, and are used as indices for measuring similarities between polygons. We then transform the extracted properties into a fuzzy format through normalization and fuzzification. Finally, the similarity graph containing the neighborhood relationship between polygons is acquired, allowing for clustering using the proposed adjacency graph model. In this paper, we clustered polygons in Staten Island, United States. The visual result and two evaluation criteria demonstrated that the EIFS-IBA similarity approach is more expressive compared to the conventional similarity (ConS) approach, generating a clustering result more consistent with human cognition. View Full-Text
Keywords: clustering; similarity approaches; EIFS-IBA clustering; similarity approaches; EIFS-IBA
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, Z.; Ma, X.; Wu, L.; Xie, Z. An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons. ISPRS Int. J. Geo-Inf. 2019, 8, 98.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top