Self-organizing dual clustering considering spatial analysis and hybrid distance measures

Jiao, Limin; Liu, Yanfang; Zou, Bin

doi:10.1007/s11430-011-4222-1

Cited by 17 publications

(15 citation statements)

References 24 publications

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“…Stratification of heterogeneity recognised by humans may be inconsistent with the true stratified heterogeneity in nature due to the limitations of human intelligence, However, stratification is still a major way to approach the nature (Wang et al, 2010b). Hundreds of classification and partition algorithms can be used to stratify heterogeneity (Lu and Carlin, 2004;Jain, 2009;Jiao et al, 2011). Examples include Kmeans grouping (Steinhaus, 1957;MacQueen, 1967;Steinley, 2006) and regression trees (Breiman et al, 1984), which are implemented in extensively used software packages, ARCGIS (©Esri Inc.) and R/SPODT.…”

Section: Introductionmentioning

confidence: 99%

A measure of spatial stratified heterogeneity

Wang

Zhang²,

2016

Ecological Indicators

1,345

608

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a b s t r a c tSpatial stratified heterogeneity, referring to the within-strata variance less than the between stratavariance, is ubiquitous in ecological phenomena, such as ecological zones and many ecological variables. Spatial stratified heterogeneity reflects the essence of nature, implies potential distinct mechanisms by strata, suggests possible determinants of the observed process, allows the representativeness of observations of the earth, and enforces the applicability of statistical inferences. In this paper, we propose a q-statistic method to measure the degree of spatial stratified heterogeneity and to test its significance. The q value is within [0,1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratification of heterogeneity). The exact probability density function is derived. The q-statistic is illustrated by two examples, wherein we assess the spatial stratified heterogeneities of a hand map and the distribution of the annual NDVI in China.

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Section: Introductionmentioning

confidence: 99%

A measure of spatial stratified heterogeneity

Wang

Zhang²,

2016

Ecological Indicators

1,345

608

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“…The dual clustering (Jiao et al 2011) can be defined as: given a set of objects { o 1 , o 2 , …, o n }, each object has two attribute domains, i.e., spatial domain and non-spatial domain, as shown in Equation 3.…”

Section: Proposed Thermal Sensor Allocation and Placement Techniquesmentioning

confidence: 99%

“…Dual clustering is the process of partitioning the object data set into several groups, while clustering dispersion in the non-spatial domain is less than the given threshold and each group is a connective cluster (Jiao et al 2011). The result of dual clustering should be spatial continuous and attributively aggregative.…”

Section: Proposed Thermal Sensor Allocation and Placement Techniquesmentioning

confidence: 99%

Research of thermal sensor allocation and placement based on dual clustering for microprocessors

Meng-tian

Liu

et al. 2013

SpringerPlus

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Dynamic thermal management techniques employ a set of on-chip thermal sensors to measure runtime thermal behavior of microprocessors so as to prevent the on-set of high temperatures. Therefore, effective analysis of thermal behavior and determination of the best allocation and placement of thermal sensors directly impact the effectiveness of the dynamic thermal management mechanisms. In this paper, we propose systematic and effective techniques for determining the fewest number of thermal sensors and the optimal locations based on dual clustering to provide a high fidelity thermal monitoring. Initially, we utilize the dual clustering algorithm to devise method that can reduce the number of sensors to a great extent while satisfying an expected accuracy. Then we identify an optimal physical location for each sensor such that the sensor’s attraction towards steep thermal gradient is maximized. Experimental results indicate the superiority of our techniques and confirm that our proposed methods are capable of creating a sensor distribution for a given microprocessor architecture using the number of thermal sensors of 2, 8, 15, 24, 35, depending on different expected hot spot temperature error accuracy of 5%, 4%, 3%, 2%, 1%, respectively.

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“…Many traditional clustering algorithms have been reported in the literature and can be grouped into six categories, i.e. density‐based (Andrade et al, ; Ester, Kriegel, Sander, & Xu, ), partitioning‐based (Kanungo et al, ), model‐based (Jiao, Hong, & Liu, ), graph‐based (Jiao, Liu, Y., & Zou, ; Liu, Deng, & Shi, ; Liu, Deng, Shi & Wang, ), grid‐based (Sheikholeslami, Chatterjee, & Zhang, ), and hierarchical algorithms (Lin, Allebach, Fan, Madan, & Dana, ). These algorithms mainly focus on either spatial or non‐spatial attributes, but rarely consider them simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wang

Liu

et al. 2016

Transactions in GIS

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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.

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Self-organizing dual clustering considering spatial analysis and hybrid distance measures

Cited by 17 publications

References 24 publications

A measure of spatial stratified heterogeneity

A measure of spatial stratified heterogeneity

Research of thermal sensor allocation and placement based on dual clustering for microprocessors

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

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