DENSITY-BASED DATA CLUSTERING METHOD
    1.
    发明申请
    DENSITY-BASED DATA CLUSTERING METHOD 有权
    基于密度的数据聚类方法

    公开(公告)号:US20120296905A1

    公开(公告)日:2012-11-22

    申请号:US13462460

    申请日:2012-05-02

    IPC分类号: G06F17/30

    CPC分类号: G06F17/30598

    摘要: A density-based data clustering method executed by a computer system is disclosed. The method includes a setup step, a clustering step, an expansion step and a termination step. The setup step sets a radius and a threshold value. The clustering step defines a single cluster on a plurality of data points of a data set, and provides and adds a plurality of first boundary marks to a seed list as seeds. The expansion step expands the cluster from each seed of the seed list, and provides and adds at least one second boundary mark to the seed list as seeds. The termination step determines whether each of the data points is clustered, wherein the clustering step is re-performed if the determination is negative.

    摘要翻译: 公开了一种由计算机系统执行的基于密度的数据聚类方法。 该方法包括设置步骤,聚类步骤,扩展步骤和终止步骤。 设置步骤设置半径和阈值。 聚类步骤在数据集合的多个数据点上定义单个簇,并且将多个第一边界标记提供并添加到种子列表中作为种子。 扩展步骤从种子列表的每个种子扩展集群,并为种子提供并添加至少一个第二个边界标记到种子列表。 终止步骤确定每个数据点是否是聚类的,其中如果确定为负,则重新执行聚类步骤。

    Density-based data clustering method
    2.
    发明授权
    Density-based data clustering method 有权
    基于密度的数据聚类方法

    公开(公告)号:US08429166B2

    公开(公告)日:2013-04-23

    申请号:US13462460

    申请日:2012-05-02

    IPC分类号: G06F7/00 G06F17/30 G06F15/16

    CPC分类号: G06F17/30598

    摘要: A density-based data clustering method executed by a computer system is disclosed. The method includes a setup step, a clustering step, an expansion step and a termination step. The setup step sets a radius and a threshold value. The clustering step defines a single cluster on a plurality of data points of a data set, and provides and adds a plurality of first boundary marks to a seed list as seeds. The expansion step expands the cluster from each seed of the seed list, and provides and adds at least one second boundary mark to the seed list as seeds. The termination step determines whether each of the data points is clustered, wherein the clustering step is re-performed if the determination is negative.

    摘要翻译: 公开了一种由计算机系统执行的基于密度的数据聚类方法。 该方法包括设置步骤,聚类步骤,扩展步骤和终止步骤。 设置步骤设置半径和阈值。 聚类步骤在数据集合的多个数据点上定义单个簇,并且将种子列表中的多个第一边界标记提供并添加到种子列表中。 扩展步骤从种子列表的每个种子扩展集群,并为种子提供并添加至少一个第二个边界标记到种子列表。 终止步骤确定每个数据点是否是聚类的,其中如果确定为负,则重新执行聚类步骤。