A density-based spatial clustering for physical constraints

Wang, Xin; Rostoker, Camilo; Hamilton, Howard J.

doi:10.1007/s10844-011-0154-7

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Due to the complexity of geographic data and the difference of data formats, present researches on spatial clustering with obstacle constraint mainly aim at clustering method for two-dimensional spatial data points [8, 10, 12–14]. There are two directions for future work.…”

Section: Discussionmentioning

confidence: 99%

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A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

Sun

Luo

Ding

et al. 2014

Computational Intelligence and Neuroscience

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An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

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Section: Discussionmentioning

confidence: 99%

“…However, it cannot discover clusters of different densities. DBRS+ is the extension of DBRS algorithm [12], considering the continuity in a neighborhood. Global parameters used by DBRS+ algorithm make it suffer from the problem of uneven density.…”

Section: Introductionmentioning

confidence: 99%

A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

Sun

Luo

Ding

et al. 2014

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Clustering is one of the most prominent data mining methods used for mining spatial information. It processes data by analyzing its spatial characteristics; spatial clustering [2] has been shown to perform well in various disciplines [3][4][5][6][7], including detecting crime hotspot distribution in crime analysis, identifying disease outbreak patterns related to public health problems, determining climate in the context of meteorological phenomena, detecting earthquake distribution in geological exploration studies, and determining the ecological landscape pattern in the ecological field. On the other hand, spatial clustering can be used as a preprocessing step for other data analysis.…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Chen

et al. 2019

IJGI

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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.

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Survey on using constraints in data mining

Grossi

Romei

Turini

2016

Data Min Knowl Disc

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This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process

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A density-based spatial clustering for physical constraints

Cited by 4 publications

References 10 publications

A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Survey on using constraints in data mining

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