Automatic clustering and boundary detection algorithm based on adaptive influence function

Nosovskiy, Gleb V.; Liu, Dongquan; Sourina, Olga

doi:10.1016/j.patcog.2008.01.021

Cited by 46 publications

(33 citation statements)

References 10 publications

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“…Simulated datasets are set according to several earlier studies about simulated dataset designation [20] and characteristics of real applications. A detailed validation of the DTSC algorithm on simulated datasets is given in Section 4.1.…”

Section: Resultsmentioning

confidence: 99%

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

Wang

Liu

Chen

et al. 2016

IJGI

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Current time series clustering algorithms fail to effectively mine clustering distribution characteristics of time series data without sufficient prior knowledge. Furthermore, these algorithms fail to simultaneously consider the spatial attributes, non-spatial time series attribute values, and non-spatial time series attribute trends. This paper proposes an adaptive density-based time series clustering (DTSC) algorithm that simultaneously considers the three above-mentioned attributes to relieve these limitations. In this algorithm, the Delaunay triangulation is first utilized in combination with particle swarm optimization (PSO) to adaptively obtain objects with similar spatial attributes. An improved density-based clustering strategy is then adopted to detect clusters with similar non-spatial time series attribute values and time series attribute trends. The effectiveness and efficiency of the DTSC algorithm are validated by experiments on simulated datasets and real applications. The results indicate that the proposed DTSC algorithm effectively detects time series clusters with arbitrary shapes and similar attributes and densities while considering noises.

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Section: Resultsmentioning

confidence: 99%

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

Wang

Liu

Chen

et al. 2016

IJGI

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“…This method involves two main procedures: The first is to adaptively construct the spatial proximity relationships by adopting the corresponding strategy from adaptive dual clustering algorithm [33]; the second is to mine clustering zones of association rules by integrating the Apriori algorithm [17] and the density-based clustering method [18]. The complete description of the MVARC method can be summarized by the following five steps (Figure 3).…”

Section: Mvarc Methodsmentioning

confidence: 99%

“…The MVARC-R-KS method is comprised of two phases. In Phase 1, the MVARC method is utilized to adaptively mine clustering zones in which the SOM distribution is significantly influenced by environmental variables; this process integrates the Delaunay triangulation, the Apriori algorithm [17], and a density-based clustering method [18]. In Phase 2, the Rank-KS method is introduced in order to select samples evenly from the clustering zones and the remaining area.…”

Section: Introductionmentioning

confidence: 99%

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

et al. 2017

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Abstract:The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank-Kennard-Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

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“…In fact, no particular clustering method has been shown to be superior to its competitors with regards to all of the necessary aspects [42]. To date, the advantages and disadvantages of various algorithms have been extensively analyzed [43][44][45][46]. Clustering attributes are the most important judgment criteria in clustering calculations.…”

Section: Layout Optimization Of Refueling Service Based On Cluster Anmentioning

confidence: 99%

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

Yang

Peng

et al. 2018

IJGI

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Abstract:Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

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Automatic clustering and boundary detection algorithm based on adaptive influence function

Cited by 46 publications

References 10 publications

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

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

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

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