An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning

Abstract

Traditional dual clustering algorithms cannot adaptively perform clustering well without sufficient prior knowledge of the dataset. This article aims at accommodating both spatial and non-spatial attributes in detecting clusters without the need to set parameters by default or prior knowledge. A novel adaptive dual clustering algorithm (ADC1) is proposed to obtain satisfactory clustering results considering the spatial proximity and attribute similarity with the presence of noise and barriers. In this algorithm, Delaunay triangulation is utilized to adaptively obtain spatial proximity and spatial homogenous patterns based on particle swarm optimization (PSO). Then, a hierarchical clustering method is employed to obtain clusters with similar attributes. The hierarchical clustering method adopts a discriminating coefficient to adaptively control the depth of the hierarchical architecture. The clustering results are further refined using an optimization approach. The advantages and practicability of the ADC1 algorithm are illustrated by experiments on both simulated datasets and real-world applications. It is found that the proposed ADC1 algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers. K E Y W O R D S adaptive dual clustering, data mining, Delaunay triangulation, hierarchical structure, rural settlement zoning 1 | I NTR OD U CTI ON Clustering algorithms aim to separate a dataset to minimize the within-group variance and maximize the betweengroup variance. As such, they are a powerful tool applied in various applications, such as in the unsupervised classification of image processing, hot spot analysis, and outlier detection. Many traditional clustering algorithms have been reported in the literature and can be grouped into six categories, i.e. density-based (Andrade et al.

A dual spatial clustering method in the presence of heterogeneity and noise

“…Future work will focus on the improvement of the DSC algorithm, including the integration of the DSC algorithm with optimization criteria techniques such as particle swarm optimization of the ADC + algorithm (Liu et al, 2016). As for the calculation of neighbor entropy, if many points of the same neighbor entropy emerge, these points will be sorted randomly in the process of implementing the expansion strategy.…”

“…Delaunay triangulation has been proved to be a powerful tool for expressing the spatial proximity of points (Liu et al, 2012; Liu, Wang, Liu, & Liu, 2016). When compared to partitioning‐ and density‐based algorithms, Delaunay triangulation with edge constraints is easily operated and can detect clusters without needing user‐specified parameters.…”

“…The Rand, Recall, and Precision indexes are adopted to assess the cluster detection approaches quantitatively. The Rand index assesses the ability of a particular cluster detection approach to find the known clusters and noises; Recall evaluates the ability of the clustering algorithm to identify positive detection success; and Precision captures the subtleties of the clustering algorithm (Liu et al, 2016).…”

An Improved Density-Based Time Series Clustering Method Based on Image Resampling: A Case Study of Surface Deformation Pattern Analysis

“…Theoretically, any two sequences are regarded as similar if all of the three measurements between them are similar. According to Liu et al [21], the similarity measurements for spatial locations and attributes should be considered interdependently. In the spatial domain, Euclidean distance is generally adopted to measure the similarity degree between sequences.…”

An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns

“…Evaluation and optimization of β are necessary and are therefore applied in this study to obtain satisfactory results under the proper value of β. The construction of proximity relationships is a graph based clustering method [14]. Hence, a graph-based evaluation function [15] that considers spatial outlier effectiveness is utilized in this phase; this method can accurately evaluate the feasibility of the spatial proximity construction results.…”