Identify Cluster

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Thomas Seidl - One of the best experts on this subject based on the ideXlab platform.

  • amtics aligning micro Clusters to Identify Cluster structures
    Database Systems for Advanced Applications, 2020
    Co-Authors: Florian Richter, Daniyal Kazempour, Thomas Seidl
    Abstract:

    OPTICS is a popular tool to analyze the Clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and Cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based Clustering specified by the height as the density threshold for Clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-Cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial Cluster instances. The offline phase reveals the current data Clustering structure with low complexity and at any time.

  • DASFAA (1) - AMTICS: Aligning Micro-Clusters to Identify Cluster Structures
    Database Systems for Advanced Applications, 2020
    Co-Authors: Florian Richter, Daniyal Kazempour, Thomas Seidl
    Abstract:

    OPTICS is a popular tool to analyze the Clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and Cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based Clustering specified by the height as the density threshold for Clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-Cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial Cluster instances. The offline phase reveals the current data Clustering structure with low complexity and at any time.

Florian Richter - One of the best experts on this subject based on the ideXlab platform.

  • amtics aligning micro Clusters to Identify Cluster structures
    Database Systems for Advanced Applications, 2020
    Co-Authors: Florian Richter, Daniyal Kazempour, Thomas Seidl
    Abstract:

    OPTICS is a popular tool to analyze the Clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and Cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based Clustering specified by the height as the density threshold for Clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-Cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial Cluster instances. The offline phase reveals the current data Clustering structure with low complexity and at any time.

  • DASFAA (1) - AMTICS: Aligning Micro-Clusters to Identify Cluster Structures
    Database Systems for Advanced Applications, 2020
    Co-Authors: Florian Richter, Daniyal Kazempour, Thomas Seidl
    Abstract:

    OPTICS is a popular tool to analyze the Clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and Cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based Clustering specified by the height as the density threshold for Clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-Cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial Cluster instances. The offline phase reveals the current data Clustering structure with low complexity and at any time.

Daniyal Kazempour - One of the best experts on this subject based on the ideXlab platform.

  • amtics aligning micro Clusters to Identify Cluster structures
    Database Systems for Advanced Applications, 2020
    Co-Authors: Florian Richter, Daniyal Kazempour, Thomas Seidl
    Abstract:

    OPTICS is a popular tool to analyze the Clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and Cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based Clustering specified by the height as the density threshold for Clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-Cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial Cluster instances. The offline phase reveals the current data Clustering structure with low complexity and at any time.

  • DASFAA (1) - AMTICS: Aligning Micro-Clusters to Identify Cluster Structures
    Database Systems for Advanced Applications, 2020
    Co-Authors: Florian Richter, Daniyal Kazempour, Thomas Seidl
    Abstract:

    OPTICS is a popular tool to analyze the Clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and Cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based Clustering specified by the height as the density threshold for Clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-Cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial Cluster instances. The offline phase reveals the current data Clustering structure with low complexity and at any time.

Lei Wang - One of the best experts on this subject based on the ideXlab platform.

  • Identifying Cluster centroids from decision graph automatically using a statistical outlier detection method
    Neurocomputing, 2019
    Co-Authors: Huanqian Yan, Lei Wang
    Abstract:

    Abstract Cluster centroid identification is a crucial step for many Clustering methods. Recently, Rodriguez and Laio have proposed an effective density-based Clustering method called Density Peak Clustering (DPC), in which the density value of each data point and the minimum distance from the points with higher density values are used to Identify Cluster centroids from the decision graph. However, there is still a lack of automatic methods for the identification of Cluster centroids from the decision graph. In this work, a novel statistical outlier detection method is designed to Identify Cluster centroids automatically from the decision graph, so that the number of Clusters is also automatically determined. In the proposed method, one-dimensional probability density functions at specific density values in the decision graph are estimated using two-dimensional Gaussian kernel functions. Then the Cluster centroids are identified automatically as outliers in the decision graph using expectation values and standard deviations computed at specific density values. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in centroid identification from the datasets with various distributions and dimensionalities. Furthermore, it is also shown that the proposed method can be effectively applied to image segmentation.

Huanqian Yan - One of the best experts on this subject based on the ideXlab platform.

  • Identifying Cluster centroids from decision graph automatically using a statistical outlier detection method
    Neurocomputing, 2019
    Co-Authors: Huanqian Yan, Lei Wang
    Abstract:

    Abstract Cluster centroid identification is a crucial step for many Clustering methods. Recently, Rodriguez and Laio have proposed an effective density-based Clustering method called Density Peak Clustering (DPC), in which the density value of each data point and the minimum distance from the points with higher density values are used to Identify Cluster centroids from the decision graph. However, there is still a lack of automatic methods for the identification of Cluster centroids from the decision graph. In this work, a novel statistical outlier detection method is designed to Identify Cluster centroids automatically from the decision graph, so that the number of Clusters is also automatically determined. In the proposed method, one-dimensional probability density functions at specific density values in the decision graph are estimated using two-dimensional Gaussian kernel functions. Then the Cluster centroids are identified automatically as outliers in the decision graph using expectation values and standard deviations computed at specific density values. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in centroid identification from the datasets with various distributions and dimensionalities. Furthermore, it is also shown that the proposed method can be effectively applied to image segmentation.