EM-Based Clustering Algorithm for Uncertain Data

Kinoshita, Naohiko; Endo, Yasunori

doi:10.1007/978-3-319-02821-7_8

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…In the Chameleon algorithm, the similarity of two nodes depends on their Euclidean distance values. e traditional Euclidean distance formula can process only those nodes whose values are continuous and discrete [50]. In the rainfall-induced regional landslide hazard assessment model, the data types of the node's attributes (landslide conditioning factors) include discrete (slope aspect), continuous (slope height), and uncertain (rainfall) values [37].…”

Section: Ca-aqd Algorithmmentioning

confidence: 99%

“…K-means and FCM algorithms can be effective if the choice of initial partitions in the prediction model is correct [49]. In fact, these parameter thresholds (every clustering center) are not easy to be set in large data sets, precisely, large study areas [50]. KPSO can break away from initial partitions dependence using iterations to identify the best cluster partitions, but it is sensitive to data clusters that have diverse shapes, densities, and sizes (called outliers and noise) [51], which restricts the advantages of using KPSO to assess landslide susceptibility in large study areas.…”

Section: Introductionmentioning

confidence: 99%

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Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Mao

Mwakapesa

Wang³

et al. 2021

Advances in Civil Engineering

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This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). The model was implemented in Baota District, Shaanxi province, China. The CA-AQD algorithm was adopted to split all grids in the study area into many groups with more similar characteristic values, which also owed to efficiently quantifying the uncertain (rainfall) value by using AQD. The K-means algorithm divides these groups into five susceptibility classes according to the values of landslide density in each group. The model was then evaluated using statistical metrics and the performance was validated and compared to that of the traditional Chameleon algorithm and KPSO algorithm. The results show that the CA-AQD algorithm attained the best performance in assessing landslide susceptibility in the study area. Thus, this work adds to the literature by introducing the first empirical integration and application of the CA-AQD algorithm to the assessment of landslides in the study area, which then is a new insight to the field. Also, the method can be helpful for dealing with landslides for better social and economic development.

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Section: Ca-aqd Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Mao

Mwakapesa

Wang³

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…However, there are some limitations in the landslide susceptibility models that are based on these clustering algorithms. k-Means and FCM algorithms can be effective if the values of the input parameters are accurate (Kinoshita and Endo 2014). KPSO does not depend on the initial values of the parameters in that it uses iterations to identify the optimal subclasses, but the results of this method are sensitive to data clusters that have arbitrary shapes (Karypia et al 1999).…”

Section: Introductionmentioning

confidence: 99%

A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms

Wang³

et al. 2020

Bull Eng Geol Environ

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Because of the strong dependence on the values for the input parameters and the cluster shape, as well as the difficulties in quantifying the precipitation in constructing landslide susceptibility maps by employing existing clustering algorithms, we propose a novel method based on an Ordering Points to Identify the Clustering Structure (OPTICS) algorithm using the Hausdorff distance (OA-HD). The OA-HD algorithm distributes mapping units into many subclasses with similar characteristic values for topography and geology. To obtain more optimal subclasses, the HD was adopted to quantify precipitation. The Kmedoids algorithm grouped these subclasses into five susceptibility levels according to the values of landslide density in each subclass. Applying the innovative integrated algorithms to the study area significantly improves the landslide susceptibility assessment, especially in a large study area. The method suggests new insights for better assessing landslide susceptibility in a large study area.

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Robust dataset classification approach based on neighbor searching and kernel fuzzy c-means

Liu

Yang

Zhou

et al. 2015

IEEE/CAA J. Autom. Sinica

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EM-Based Clustering Algorithm for Uncertain Data

Cited by 3 publications

References 4 publications

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms

Robust dataset classification approach based on neighbor searching and kernel fuzzy c-means

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