zoukankan      html  css  js  c++  java
  • DBSCAN(Density-based spatial clustering of applications with noise)

    Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.[2]

    In 2014, the algorithm was awarded the test of time award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, KDD.[3]

    Contents
    1 Preliminary
    2 Algorithm
    3 Complexity
    4 Advantages
    5 Disadvantages
    6 Parameter estimation
    7 Extensions
    8 Availability
    9 See also
    10 Notes
    11 References
    11.1 Further readin

    Preliminary

    Consider a set of points in some space to be clustered. For the purpose of DBSCAN clustering, the points are classified as core points, (density-)reachable points and outliers, as follows:

    A point p is a core point if at least minPts points are within distance ε(ε is the maximum radius of the neighborhood from p) of it (including p). Those points are said to be directly reachable from p. By definition, no points are directly reachable from a non-core point.
    A point q is reachable from p if there is a path p1, ..., pn with p1 = p and pn = q, where each pi+1 is directly reachable from pi (all the points on the path must be core points, with the possible exception of q).
    All points not reachable from any other point are outliers.
    Now if p is a core point, then it forms a cluster together with all points (core or non-core) that are reachable from it. Each cluster contains at least one core point; non-core points can be part of a cluster, but they form its "edge", since they cannot be used to reach more points.

    wiki: https://en.wikipedia.org/wiki/DBSCAN

  • 相关阅读:
    基于 HTML5 Canvas 的交互式地铁线路图
    基于HTML5的WebGL实现json和echarts图表展现在同一个界面
    基于HTML5和WebGL的3D网络拓扑结构图
    JavaScript基础:创建对象
    使用ksoap2报java.io.EOFException异常问题解决方法
    Silverlight之我见
    今年的IT大趋势是虚拟现实
    Apache服务器部署ASP.NET网站
    Bootstrap优秀网站:乐窝网
    [转载]C#读取Excel几种方法的体会
  • 原文地址:https://www.cnblogs.com/wangduo/p/6131916.html
Copyright © 2011-2022 走看看