Name
Title | ||
|---|---|---|
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 (ADC+) 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 ADC+ algorithm are illustrated by experiments on both simulated datasets and real-world applications. It is found that the proposed ADC+ algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1111/tgis.12246 | TRANSACTIONS IN GIS |
Keywords | Field | DocType |
adaptive dual clustering,data mining,Delaunay triangulation,hierarchical structure,rural settlement zoning | Hierarchical clustering,Data mining,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Correlation clustering,Computer science,Determining the number of clusters in a data set,Cluster analysis,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
21.0 | 5.0 | 1361-1682 |
Citations | PageRank | References |
2 | 0.42 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaolin Liu | 1 | 97 | 25.42 |
| Xiaomi Wang | 2 | 2 | 0.42 |
| Dianfeng Liu | 3 | 2 | 0.75 |
| Leilei Liu | 4 | 2 | 0.42 |