无参数聚类边界检测算法的研究

为自动快速地提取聚类的边界点,减少输入参数对边界检测结果的影响,提出一种无参数聚类边界检测算法。该算法不需要任何参数,在生成的三角剖分图上计算每个数据点的边界度,用k-means自动计算边界度阈值,按边界度阈值将数据集划分为候选边界点和非候选边界点两部分,根据噪声点在三角剖分图中的性质去除候选边界点中的噪声点,最终检测出边界点。实验结果表明,该算法能快速、有效地识别任意形状、不同大小和密度聚类的边界点。     

基于统计信息的聚类边界模式检测算法

为有效地检测聚类的边界点,提出基于统计信息的边界模式检测算法。根据数据对象的k距离统计信息设定邻域半径,再利用对象邻域范围内邻居的k距离统计信息寻找边界点。实验结果表明,该算法可以有效地检测出任意形状、不同大小和不同密度聚类的边界点,并可以消除噪声。     

基于变异系数的边界点检测算法

为有效检测聚类的边界点,提出基于变异系数的边界点检测算法.首先计算出数据对象到它的k-距离邻居距离之和的平均值.然后用平均值的倒数作为每个点的密度,通过变异系数刻画数据对象密度分布特征寻找边界点.实验结果表明,该算法可在含有任意形状、不同大小和不同密度的数据集上快速有效检测出聚类的边界点,并可消除噪声.     

基于统计信息聚类边界的不平衡数据分类方法

为解决不平衡数据在传统处理方法中容易出现数据的过拟合和欠拟合问题,提出基于统计信息聚类边界的不平衡数据分类方法.去除数据中噪声点,根据数据对象的k距离设定邻域半径,利用对象邻域范围内的k距离统计信息寻找边界点与非边界点;将少数类中的边界点作为样本,采用SMOTE算法进行过采样,对多数类采用基于距离的欠采样删除远离边界的...     

分类数据的聚类边界检测技术

随着分类属性数据集的应用越来越广泛,获取含有分类属性数据集的聚类边界的需求也越来越迫切。为了获取聚类的边界,在定义分类数据的边界度和聚类边界的基础上,提出了一种带分类属性数据的聚类边界检测算法——CBORDER。该算法首先利用随机分配初始聚类中心和边界度对类进行划分并获取记录边界点的证据,然后运用证据积累的思想多次执行该过程来获取聚类的边界。实验结果表明,CBORDER算法能有效地检测出高维分类属性数据集中聚类的边界。     

车祸中车辆严重碰撞区域边界图像分割技术

研究车祸中严重碰撞车辆图像边界准确分割问题。车祸中,如果发生碰撞较为严重,两车图像碰撞部位交汇的像素分布较为密集,像素会产生变异。传统的边沿检测算法多是基于像素差异进行边界分割,当车祸中像素密度分布密集导致像素变异的情况,会造成像素聚类效果不好,分割不完整,分割的准确性不高。提出了一种基于密度分布函数的车祸图像边界检测算法。算法通过计算数据车祸像素邻域半径内每个像素点对它的高斯影响函数之和,将其作为该像素对象的密度,再通过变异系数刻画像素对象密度分布特征从而提取车祸图像边界点。实验结果表明,算法提高了车祸图像边界分割的准确度。     

混合属性数据集的聚类边界检测技术

为了满足数据分析中获取含有混合属性的数据集聚类的边界需求, 提出一种混合属性数据集的聚类边界检测算法(BERGE). 该算法利用模糊聚类隶属度定义边界因子以识别候选边界集, 然后运用证据积累的思想提取聚类的边界. 在综合数据集和真实数据集上的实验结果表明, BERGE 算法能有效地检测混合属性数据集、数值属性数据集以及分类属性数据集的聚类边界, 与现有同类算法相比具有更高的精度.     

网格聚类中的边界处理技术

提出利用限制性k近邻和相对密度的概念识别网格聚类边界点的技术,给出网格聚类中的边界处理算法和带边界处理的网格聚类算法(GBCB).实验表明,聚类边界处理技术精度高,能有效地将聚类的边界点和孤立点/噪声数据分离开来.基于该边界处理技术的网格聚类算法GBCB能识别任意形状的聚类.由于它只对数据集进行一遍扫描,算法的运行时间是输入数据大小的线性函数,可扩展性好.     

求解K-means聚类更有效的算法

聚类分析是数据挖掘及机器学习领域内的重点问题之一.K-means聚类由于其简羊买用,在聚类划分中是应用最广泛的一种方案.提出了在传统的K-means算法中初始点选取的新方案,对于K-means收敛计算时利用三角不等式,提出了加速收敛过程的改进方案.实验结果表明,改进后的新方法相对于传统K-means聚类所求的结果有较好的聚类划分.     

基于网格熵的边界点检测算法

为了快速有效地检测聚类的边界点,提出了网格熵的概念和基于网格熵的边界点检测算法Greb。该算法利用网格熵的大小来判定聚类的边界点,且只对数据集进行两遍扫描。实验结果表明,对含有任意形状、不同大小以及不同密度且带有噪声的数据集,该算法能快速有效地检测出聚类的边界点。     

Keyframe-based video summarization using Delaunay clustering

Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique.     

三维约束Delaunay三角化的实现

分析了约束Delaunay三角化中存在的边界一致性问题,给出了约束Delaunay三角化的理论依据,重点探讨了三维约束Delaunay三角化的可行性条件和范围,同时,给出了三维有限域约束Delaunay三角化的实现方法及其在石油地质勘探数据和机械零件方面的网格剖分实例.这种算法在复杂对象的科学计算和工程分析中发挥了重要作用.     

An automatic shape independent clustering technique

This article describes a clustering technique that can automatically detect any number of well-separated clusters which may be of any shape, convex and/or non-convex. This is in contrast to most other techniques which assume a value for the number of clusters and/or a particular cluster structure. The proposed technique is based on an iterative partitioning of the relative neighborhood graph, coupled with a post-processing step for merging small clusters. Techniques for improving the efficiency of the proposed scheme are implemented. The clustering scheme is able to detect outliers in data. It is also able to indicate the inherent hierarchical nature of the clusters present in a data set. Moreover, the proposed technique is also able to identify the situation when the data do not have any natural clusters at all. Results demonstrating the effectiveness of the clustering scheme are provided for several data sets.     

二维复杂限定Delaunay三角化算法

针对包括曲线边界和内部带有曲线限定条件的二维Delaunay三角化问题,提出了一种细化算法.首先给出了曲线段的逼近边定义,以保证限定曲线在网格中的存在;然后证明了该算法的收敛性和最终曲线的逼近边集合与原曲线的拓扑一致性,并且生成的网格符合Delaunay优化准则;最后给出了算法的应用实例,验证了其有效性.     

三维Delaunay三角剖分快速点定位算法研究

提高点定位的速度是提高Delaunay三角剖分运行效率的关键。本文对四面体定位算法进行了研究,结合有向查找定位的技术,建立合理的数据结构,通过对每个搜索四面体只需计算三个面的法向量,优化了基于法向定位的算法,从减少算法中运算量的角度提高运行效率。该算法定位路径唯一,效率更高,而且具有较好的效果。     

An adaptive spatial clustering algorithm based on delaunay triangulation

In this paper, an adaptive spatial clustering algorithm based on Delaunay triangulation (ASCDT for short) is proposed. The ASCDT algorithm employs both statistical features of the edges of Delaunay triangulation and a novel spatial proximity definition based upon Delaunay triangulation to detect spatial clusters. Normally, this algorithm can automatically discover clusters of complicated shapes, and non-homogeneous densities in a spatial database, without the need to set parameters or prior knowledge. The user can also modify the parameter to fit with special applications. In addition, the algorithm is robust to noise. Experiments on both simulated and real-world spatial databases (i.e. an earthquake dataset in China) are utilized to demonstrate the effectiveness and advantages of the ASCDT algorithm.     

Efficient parallel implementations of near Delaunay triangulation with High Performance Fortran

Unstructured mesh generation exposes highly irregular computation patterns, which imposes a challenge in implementing triangulation algorithms on parallel machines. This paper reports on an efficient parallel implementation of near Delaunay triangulation with High Performance Fortran (HPF). Our algorithm exploits embarrassing parallelism by performing sub‐block triangulation and boundary merge independently at the same time. The sub‐block triangulation is a divide & conquer Delaunay algorithm known for its sequential efficiency, and the boundary triangulation is an incremental construction algorithm with low overhead. Compared with prior work, our parallelization method is both simple and efficient. In the paper, we also describe a solution to the collinear points problem that usually arises in large data sets. Our experiences with the HPF implementation show that with careful control of the data distribution, we are able to parallelize the program using HPF's standard directives and extrinsic procedures. Experimental results on several parallel platforms, including an IBM SP2 and a DEC Alpha farm, show that a parallel efficiency of 42–86% can be achieved for an eight‐node distributed memory system. We also compare efficiency of the HPF implementation with that of a similarly hand‐coded MPI implementation. Copyright © 2004 John Wiley & Sons, Ltd.     

基于Voronoi图和三角剖分的闭合曲线重建

以Voronoi图和Delaunay三角剖分为基础,针对二维闭合曲线集的采样点集,提出一种曲线重建算法。该算法按给定采样密度对曲线集进行采样,从而用一条或多条线段准确地重建曲线集,将采样点密集程度的度量定义为点集的本地特征值度量,以此要求采样达到一定的密集程度。理论分析证明该算法的时间复杂度为O(nlogn)。     

Reconstruction of 2D polygonal curves and 3D triangular surfaces via clustering of Delaunay circles/spheres

A simple and efficient method is presented in this paper to reliably reconstruct 2D polygonal curves and 3D triangular surfaces from discrete points based on the respective clustering of Delaunay circles and spheres. A Delaunay circle is the circumcircle of a Delaunay triangle in the 2D space, and a Delaunay sphere is the circumsphere of a Delaunay tetrahedron in the 3D space. The basic concept of the presented method is that all the incident Delaunay circles/spheres of a point are supposed to be clustered into two groups along the original curve/surface with satisfactory point density. The required point density is considered equivalent to that of meeting the well-documented r-sampling condition. With the clustering of Delaunay circles/spheres at each point, an initial partial mesh can be generated. An extrapolation heuristic is then applied to reconstructing the remainder mesh, often around sharp corners. This leads to the unique benefit of the presented method that point density around sharp corners does not have to be infinite. Implementation results have shown that the presented method can correctly reconstruct 2D curves and 3D surfaces for known point cloud data sets employed in the literature.