A distance-relatedness dynamic model for clustering high dimensional data of arbitrary shapes and densities

It is important to find the natural clusters in high dimensional data where visualization becomes difficult. A natural cluster is a cluster of any shape and density, and it should not be restricted to a globular shape as a wide number of algorithms assume, or to a specific user-defined density as some density-based algorithms require.In this work, it is proposed to solve the problem by maximizing the relatedness of distances between patterns in the same cluster. It is then possible to distinguish clusters based on their distance-based densities. A novel dynamic model is proposed based on new distance-relatedness measures and clustering criteria. The proposed algorithm “Mitosis” is able to discover clusters of arbitrary shapes and arbitrary densities in high dimensional data. It has a good computational complexity compared to related algorithms. It performs very well on high dimensional data, discovering clusters that cannot be found by known algorithms. It also identifies outliers in the data as a by-product of the cluster formation process. A validity measure that depends on the main clustering criterion is also proposed to tune the algorithm's parameters. The theoretical bases of the algorithm and its steps are presented. Its performance is illustrated by comparing it with related algorithms on several data sets.     

从空间数据库发现聚类：一种基于数学形态学的算法

聚类算法能从空间数据库中直接发现一些有意义的聚类结构而不需要背景知识，是空间数据发掘和知识发现的重要手段。在分析已有聚类算法的基础上，提出了一种基于数学形态学的聚类算法，该算法能够处理任意形状的聚类，采用启发式方法自动确定最优聚类数。同时，该算法也可以在矢量型空间数据库中得到实现。试验表明算法是可行和有效的，且能处理存在噪音的数据。     

A new hybrid method based on partitioning-based DBSCAN and ant clustering

Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. It can discover clusters with arbitrary shapes and only requires two input parameters. DBSCAN has been proved to be very effective for analyzing large and complex spatial databases. However, DBSCAN needs large volume of memory support and often has difficulties with high-dimensional data and clusters of very different densities. So, partitioning-based DBSCAN algorithm (PDBSCAN) was proposed to solve these problems. But PDBSCAN will get poor result when the density of data is non-uniform. Meanwhile, to some extent, DBSCAN and PDBSCAN are both sensitive to the initial parameters. In this paper, we propose a new hybrid algorithm based on PDBSCAN. We use modified ant clustering algorithm (ACA) and design a new partitioning algorithm based on ‘point density’ (PD) in data preprocessing phase. We name the new hybrid algorithm PACA-DBSCAN. The performance of PACA-DBSCAN is compared with DBSCAN and PDBSCAN on five data sets. Experimental results indicate the superiority of PACA-DBSCAN algorithm.     

基于局部中心度量的边界点划分密度聚类算法

针对聚类算法在检测任意簇时精确度不高、迭代次数多及效果不佳等缺点,提出了基于局部中心度量的边界点划分密度聚类算法——DBLCM.在局部中心度量的限制下,数据点被划分到核心区域或边界区域.核心区域的点按照互近邻优先成簇的分配方式形成初始簇,边界区域的点参考互近邻中距离最近点所在簇进行分配,从而得到最终簇.为验证算法的有效性,将DBLCM与3个经典算法和3个近几年新提出的优秀算法,在包含任意形状、任意密度的二维数据集和任意维度的多维数据集上进行测试.另外,为了验证DBLCM算法中参数k的敏感性,在所用的数据集上做了k值与簇质量的相关性测试.实验结果表明,DBLCM算法具有识别精度高,检测任意簇效果好和无需迭代等优点,综合性能优于6个对比算法.     

SUDBC:一种基于空间单元密度的快速聚类算法

随着数据规模越来越大,要求聚类算法有很高的执行效率,很好的扩展性,能发现任意形状的聚类以及对噪音数据的不敏感性.提出了一种基于空间单元密度的快速聚类算法SUDBC,该算法首先将被聚类的数据划分成若干个空间单元,然后基于空间单元密度将密度超过给定阈值的邻居单元合并为一个类.实验结果验证了SUDBC算法具有处理任意形状的数据和对噪音数据不敏感的特点.     

结构复杂数据的半监督聚类

基于成对限制,提出一种半监督聚类算法（ＳＣＣＤ）,它能够处理存在多种密度结构复杂的数据且识别任意形状的簇．利用成对限制反映的多密度分布信息计算基于密度的聚类算法（ＤＢＳＣＡＮ）的邻域半径参数Ｅｐｓ,并利用不同参数的ＤＢＳＣＡＮ 算法处理复杂形状且密度变化的数据集．实验结果表明,ＳＣＣＤ 算法能在噪声环境下发现任意形状且多密度的簇,性能优于已有同类算法．     

基于密度复杂簇聚类算法研究与实现

聚类算法在模式识别、数据分析、图像处理、以及市场研究的应用中,需要解决的关键技术是如何有效地聚类各种复杂的数据对象簇。在分析与研究现有聚类算法的基础上,提出了一种基于密度和自适应密度可达的改进算法。实验证明,该算法能够有效聚类任意分布形状、不同密度、不同尺度的簇;同时,算法的计算复杂度与传统基于密度的聚类算法相比有明显的降低。     

Cure: an efficient clustering algorithm for large databases

Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new clustering algorithm called CURE that is more robust to outliers, and identifies clusters having non-spherical shapes and wide variances in size. CURE achieves this by representing each cluster by a certain fixed number of points that are generated by selecting well scattered points from the cluster and then shrinking them toward the center of the cluster by a specified fraction. Having more than one representative point per cluster allows CURE to adjust well to the geometry of non-spherical shapes and the shrinking helps to dampen the effects of outliers. To handle large databases, CURE employs a combination of random sampling and partitioning. A random sample drawn from the data set is first partitioned and each partition is partially clustered. The partial clusters are then clustered in a second pass to yield the desired clusters. Our experimental results confirm that the quality of clusters produced by CURE is much better than those found by existing algorithms. Furthermore, they demonstrate that random sampling and partitioning enable CURE to not only outperform existing algorithms but also to scale well for large databases without sacrificing clustering quality.     

面向复杂簇的聚类算法研究与实现

有效聚类各种复杂的数据对象簇是聚类算法应用干事务对象划分、图像分割、机器学习等方面需要解决的关键技术.在分析与研究现有聚类算法的基础上,提出一种基于密度和自适应密度可达的改进算法.实验证明,该算法能够有效聚类任意分布形状、不同密度、不同尺度的簇;同时,算法的计算复杂度与传统基于密度的聚类算法相比有明显的降低.     

基于划分与层次方法的混合聚类算法

为了更好地实现聚类,在汲取传统的划分算法、层次算法特性的基础上,提出了一种新的基于划分和层次的混合聚类算法（MPH）,该算法将聚类的过程分为分裂和合并两个阶段,在分裂阶段反复采用k-means算法,将数据集划分为多个同质的子簇,在合并阶段采用凝聚的层次聚类算法。实验表明,该算法能够发现任意形状、任意大小的聚类,并且对噪声点不敏感。     

一种基于网格和最小生成树的数据流聚类算法

针对CluStream算法对非球状簇聚类的不足,同时基于均匀网格划分的聚类算法多数是以降低聚类精度为代价来提高聚类效率,给出了一种新的数据流聚类算法一GTSClu算法,该算法是基于网格的最小生成树(MST)数据流聚类算法.算法分为在线处理与离线聚类两部分,并运用了网格拆分与最小生成树技术,可以有效排除噪声数据,发现任意...     

基于网格和最近邻居的聚类算法

针对目前已有的聚类算法不能很好地处理包含不同密度的簇数据,或者不能很好地区分相邻的密度相差不大的簇的问题,提出1种新的基于严格最近邻居和共享最近邻居的聚类算法．通过构造共享严格最近邻图,使样本点在密度一致的区域保持连接,而在密度不同的相邻区域断开连接,并尽可能去除噪声点和孤立点．该算法可以处理包含有不同密度的簇数据,而且在处理高维数据时具有较低的时间复杂度、实验结果证明,该算法能有效找出不同大小、形状和密度的聚类．     

Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index

Classical clustering methods, such as partitioning and hierarchical clustering algorithms, often fail to deliver satisfactory results, given clusters of arbitrary shapes. Motivated by a clustering validity index based on inter-cluster and intra-cluster density, we propose that the clustering validity index be used not only globally to find optimal partitions of input data, but also locally to determine which two neighboring clusters are to be merged in a hierarchical clustering of Self-Organizing Map (SOM). A new two-level SOM-based clustering algorithm using the clustering validity index is also proposed. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data in a better way than classical clustering algorithms on an SOM.     

基于数学形态学的聚类集成算法

提出了基于数学形态学的聚类集成算法CEOMM.它利用不同的结构元素的探针作用,对不同的结构元素探测出来的簇核心图进行集成,在集成所得到的簇核心基础上聚类.实验结果表明,算法CEOMM对有复杂类形状的数据集进行聚类时,效果比传统聚类算法更好,且能确定聚类数.而且由于采用了不同的结构元素进行探测,对于由不同形状的类构成的数据集其聚类效果很理想.     

基于空间邻近的点目标聚类方法

空间聚类是空间数据挖掘和知识发现领域的主要研究方向之一,但点目标空间分布密度的不均匀、分布形状的多样化,以及"多桥"链接问题的存在,使得基于距离和密度的聚类算法不能高效且有效地识别聚集性高的点目标。提出了基于空间邻近的点目标聚类方法,通过Voronoi建模识别点目标间的空间邻近关系,并以Voronoi势力范围来定义相似度准则,最终构建树结构以实现点目标的聚集模式识别。实验将所提算法与K-means、具有噪声的基于密度的聚类(DBSCAN)算法进行比较分析,结果表明算法能够发现密度不均且任意形状分布的点目标集群,同时准确划分"桥"链接的簇,适用于空间点目标异质分布下的聚集模式识别。     

On Density-Based Data Streams Clustering Algorithms: A Survey简

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the nmnber of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density- based methods in the clustering process and at the same time overcoming the constraints, which are put out by data streanFs nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms＇ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.     

Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density

The self-organizing map (SOM) has been widely used in many industrial applications. Classical clustering methods based on the SOM often fail to deliver satisfactory results, specially when clusters have arbitrary shapes. In this paper, through some preprocessing techniques for filtering out noises and outliers, we propose a new two-level SOM-based clustering algorithm using a clustering validity index based on inter-cluster and intra-cluster density. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data better than the classical clustering algorithms based on the SOM, and find an optimal number of clusters.     

基于人工免疫系统aiNet模型的层次聚类算法

提出了一种基于人工免疫系统重要模型aiNet模型的层次聚类算法aiNHA。该算法首先采用aiNet的方法生成抗体的记忆细胞矩体和相似性矩阵,这样就将数据集划分为若干子簇。再按照层次聚类的方法,合并连接相似度高的子簇,得到最终的聚类结果。该算法适用于发现任意形状的聚类簇,并且继承了免疫算法搜索速度快、效率高的优点。     

一种改进的基于密度的抽样聚类算法

基于密度的聚类算法DBSCAN是一种有效的空间聚类算法,它能够发现任意形状的聚类并且有效地处理噪声。然而,DBSCAN算法也有一些缺点,例如,①在聚类时只考虑空间属性没有考虑非空间属性;②在对大规模空间数据库进行聚类分析时需要较大的内存支持和I/O消耗。为此,在分析DBSCAN算法不足的基础上,提出了一种改进的基于密度的抽样聚类(improved density-based spatial clustering algorithm with sampling,IDBSCAS)算法,使之能够有效地处理大规模空间数据库,并且它不仅考虑了空间属性也考虑了非空间属性。2维空间数据的测试结果表明,该算法是可行、有效的。