改进的混合属性数据聚类算法

k-prototypes是目前处理数值属性和分类属性混合数据主要的聚类算法,但其聚类结果对初值有明显的依赖性.对k-prototypes初值选取方法进行了分析和研究,提出一种新的改进方法.该方法有更高的稳定性和较强的伸缩性,可减少一定程度的上随机性.实际数据集仿真结果表明,改进算法是正确和有效的.     

混合属性数据k-prototypes聚类算法

在现实世界中经常遇到混合数值属性和分类属性的数据, k-prototypes是聚类该类型数据的主要算法之一。针对现有混合属性聚类算法的不足,提出一种基于分布式质心和新差异测度的改进的 k-prototypes 算法。在新算法中,首先引入分布式质心来表示簇中的分类属性的簇中心,然后结合均值和分布式质心来表示混合属性的簇中心,并提出一种新的差异测度来计算数据对象与簇中心的距离,新差异测度考虑了不同属性在聚类过程中的重要性。在三个真实数据集上的仿真实验表明,与传统的聚类算法相比,本文算法的聚类精度要优于传统的聚类算法,从而验证了本文算法的有效性。     

一种有效的Gk-prototypes聚类算法

针对传统的聚类算法对初始聚类中心敏感、只能对单一属性聚类且聚类效果有时欠佳等不足,提出了一种能处理数值属性和分类属性的Gk-prototypes聚类算法。在经典的k-prototypes聚类算法的基础上,利用去模糊相似矩阵来构造粗粒子集,结合粒计算和最大最小距离法确定初始聚类中心,并改进了目标函数。实验结果和理论分析表明,Gk-prototypes聚类算法与其他基于k-prototypes的改进算法相比,聚类更准确,有效性更好,鲁棒性更强。     

融合单纯形映射与熵加权的聚类方法

由于分类型和数值型属性特性的差异,设计混合类型数据聚类算法时通常需要对两种类型属性区别对待,增加了聚类算法的设计与实现难度。另外,不同属性所包含的信息量存在差异,但现有算法通常平等对待各个属性。提出了一种融合单纯形映射与信息熵加权的混合类型数据聚类算法。基于单纯形理论将分类型属性映射为高维数值属性向量,应用信息熵理论为各属性分配权重建立相似性度量公式,将该度量方法应用于K-Means算法框架得到聚类算法。在6个UCI的混合数据集上的实验表明,提出的聚类算法优于传统映射聚类算法和K-Prototype算法,在准确度上分别提高了2.70％和18.33％。     

混合属性数据流的二重k近邻聚类算法

现有的数据流聚类算法大都只能处理单一数值属性的数据,不能应对同时包含数值属性与分类属性特征的数据,且已存在的混合属性数据流聚类算法在对数据的标准化处理和聚类上还有很大的改进之处,为此,提出二重k近邻混合属性数据流聚类算法.该算法采用CluStream算法的在线、离线框架,并提出了混合属性数据流下三步聚类的思想.算法先运用二重k近邻和改进的维度距离生成微聚类,然后利用动态标准化数据方法和基于均值的余弦模型生成初始宏聚类,最后利用基于均值的余弦模型和先验聚类结果进行宏聚类优化.实验结果表明,所提出的算法具有良好的聚类质量及可扩展性.     

一种基于信息熵的混合属性数据谱聚类算法

针对传统的聚类算法只能处理单属性的数据,不能很好地处理混合属性数据的聚类问题,以及目前大多数混合属性数据聚类算法对初始化敏感、不能处理任意形状的数据的问题,提出一种基于信息熵的混合属性数据谱聚类算法,用于处理混合类型数据。首先,提出了一种新的相似性度量方式,利用谱聚类算法中的数值型数据构成的高斯核函数矩阵与新的基于信息熵的分类型数据构成的影响因子矩阵相结合代替了传统的相似度矩阵,新的相似度矩阵避免了数值属性与分类属性数据之间的转换和参数调整;然后,把新的相似度矩阵运用到谱聚类算法中,以便于处理任意形状的数据,最终得出聚类结果。通过在UCI的数据集上的实验表明,该算法能有效地处理混合属性数据的聚类问题,且具有较高的稳定性以及良好的鲁棒性。     

数值型和分类型混合数据的模糊K-Prototypes聚类算法

由于数据库经常同时包含数值型和分类型的属性,因此研究能够处理混合型数据的聚类算法无疑是很重要的.讨论了混合型数据的聚类问题,提出了一种模糊K-prototypes算法.该算法融合了K-means和K-modes对数值型和分类型数据的处理方法,能够处理混合类型的数据.模糊技术体现聚类的边界特征,更适合处理含有噪声和缺失数据的数据库.实验结果显示,模糊算法比相应的确定算法得到的结果准确度高.     

基于密度和混合距离度量方法的混合属性数据聚类研究

针对基于密度的传统算法不能处理混合属性数据,以及目前的混合属性聚类算法大多数聚类质量不高等问题,提出了基于密度和混合距离度量方法的混合属性聚类算法.该算法通过分析混合属性数据特征,将混合属性数据分为数值占优、分类占优和均衡型混合属性数据3类,分析不同情况的特征选取相应的距离度量方式,通过预设参数能够发现数据密集区域,确定核心点,再利用核心点确定密度相连的对象实现聚类,获得最终的聚类结果.将算法应用于多种数据集上的实验结果表明,该算法具有较高的聚类质量,能够有效处理混合属性数据.     

基于EM算法的高斯混合模型在鸢尾花数据集的应用

高斯混合模型是一种含隐变量的概率图模型,其参数通常由EM算法迭代训练得到.本文在简单推导高斯混合模型的EM算法后,将使用高斯混合模型对鸢尾花(iris)数据集进行分类判别.同时,针对EM算法受初始值影响大的问题,使用了K均值聚类算法作为其初始值的估计方法.在得到K均值聚类算法和EM算法的分类判别结果后,对比两种算法的判...     

基于快速求解高斯混合模型的流量聚类算法

基于聚类算法可以对多个属性聚类的特点,提出一种基于快速求解高斯混合模型的聚类算法,用于研究网络流量的分类,使其达到更佳的聚类效果。通过与其他算法比较,讨论了该种方法在流量聚类中的适用性。仿真结果表明,该方法聚类精度高,经过初始聚类中心后的EM算法用于求解GMM有较高的估算准确性,有效地提高了EM算法的收敛速度。     

K-Harmonic means type clustering algorithm for mixed datasets

K-means type clustering algorithms for mixed data that consists of numeric and categorical attributes suffer from cluster center initialization problem. The final clustering results depend upon the initial cluster centers. Random cluster center initialization is a popular initialization technique. However, clustering results are not consistent with different cluster center initializations. K-Harmonic means clustering algorithm tries to overcome this problem for pure numeric data. In this paper, we extend the K-Harmonic means clustering algorithm for mixed datasets. We propose a definition for a cluster center and a distance measure. These cluster centers and the distance measure are used with the cost function of K-Harmonic means clustering algorithm in the proposed algorithm. Experiments were carried out with pure categorical datasets and mixed datasets. Results suggest that the proposed clustering algorithm is quite insensitive to the cluster center initialization problem. Comparative studies with other clustering algorithms show that the proposed algorithm produce better clustering results.     

Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm to categorical domains and domains with mixed numeric and categorical values. The k-modes algorithm uses a simple matching dissimilarity measure to deal with categorical objects, replaces the means of clusters with modes, and uses a frequency-based method to update modes in the clustering process to minimise the clustering cost function. With these extensions the k-modes algorithm enables the clustering of categorical data in a fashion similar to k-means. The k-prototypes algorithm, through the definition of a combined dissimilarity measure, further integrates the k-means and k-modes algorithms to allow for clustering objects described by mixed numeric and categorical attributes. We use the well known soybean disease and credit approval data sets to demonstrate the clustering performance of the two algorithms. Our experiments on two real world data sets with half a million objects each show that the two algorithms are efficient when clustering large data sets, which is critical to data mining applications.     

A fuzzy c-means-type algorithm for clustering of data with mixed numeric and categorical attributes employing a probabilistic dissimilarity functional

Gath–Geva (GG) algorithm is one of the most popular methodologies for fuzzy c-means (FCM)-type clustering of data comprising numeric attributes; it is based on the assumption of data deriving from clusters of Gaussian form, a much more flexible construction compared to the spherical clusters assumption of the original FCM. In this paper, we introduce an extension of the GG algorithm to allow for the effective handling of data with mixed numeric and categorical attributes. Traditionally, fuzzy clustering of such data is conducted by means of the fuzzy k-prototypes algorithm, which merely consists in the execution of the original FCM algorithm using a different dissimilarity functional, suitable for attributes with mixed numeric and categorical attributes. On the contrary, in this work we provide a novel FCM-type algorithm employing a fully probabilistic dissimilarity functional for handling data with mixed-type attributes. Our approach utilizes a fuzzy objective function regularized by Kullback–Leibler (KL) divergence information, and is formulated on the basis of a set of probabilistic assumptions regarding the form of the derived clusters. We evaluate the efficacy of the proposed approach using benchmark data, and we compare it with competing fuzzy and non-fuzzy clustering algorithms.     

A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

Almost all subspace clustering algorithms proposed so far are designed for numeric datasets. In this paper, we present a k-means type clustering algorithm that finds clusters in data subspaces in mixed numeric and categorical datasets. In this method, we compute attributes contribution to different clusters. We propose a new cost function for a k-means type algorithm. One of the advantages of this algorithm is its complexity which is linear with respect to the number of the data points. This algorithm is also useful in describing the cluster formation in terms of attributes contribution to different clusters. The algorithm is tested on various synthetic and real datasets to show its effectiveness. The clustering results are explained by using attributes weights in the clusters. The clustering results are also compared with published results.     

基于密度的聚类中心自动确定的混合属性数据聚类算法研究

面对广泛存在的混合属性数据,现有大部分混合属性聚类算法普遍存在聚类 质量低、聚类算法参数依赖性大、聚类类别个数和聚类中心无法准确自动确定等问题,针对 这些问题本文提出了一种基于密度的聚类中心自动确定的混合属性数据 聚类算法.该算法通过分析混合属性数据特征,将混合属性数据分为数 值占优、分类占优和均衡型混合属性数据三类,分析不同情况的特征选取 相应的距离度量方式.在计算数据集各个点的密度和距离分布图基础 上,深入分析获得规律: 高密度且与比它更高密度的数据点有较大距离的数 据点最可能成为聚类中心,通过线性回归模型和残差分析确定奇异 点,理论论证这些奇异点即为聚类中心,从而实现了自动确定聚类中心.采 用粒子群算法(Particle swarm optimization, PSO)寻找最优dc值,通过参数dc能够计算得到 任意数据对象的密度和到比它密度更高的点的最小距离,根据聚类 中心自动确定方法确定每个簇中心,并将其他点按到最近邻的更高 密度对象的最小距离划分到相应的簇中,从而实现聚类.最终将本文 提出算法与其他现有的多种混合属性聚类算法在多个数据集上进行 算法性能比较,验证本文提出算法具有较高的聚类质量.     

Determining the number of clusters using information entropy for mixed data

In cluster analysis, one of the most challenging and difficult problems is the determination of the number of clusters in a data set, which is a basic input parameter for most clustering algorithms. To solve this problem, many algorithms have been proposed for either numerical or categorical data sets. However, these algorithms are not very effective for a mixed data set containing both numerical attributes and categorical attributes. To overcome this deficiency, a generalized mechanism is presented in this paper by integrating Rényi entropy and complement entropy together. The mechanism is able to uniformly characterize within-cluster entropy and between-cluster entropy and to identify the worst cluster in a mixed data set. In order to evaluate the clustering results for mixed data, an effective cluster validity index is also defined in this paper. Furthermore, by introducing a new dissimilarity measure into the k-prototypes algorithm, we develop an algorithm to determine the number of clusters in a mixed data set. The performance of the algorithm has been studied on several synthetic and real world data sets. The comparisons with other clustering algorithms show that the proposed algorithm is more effective in detecting the optimal number of clusters and generates better clustering results.     

动态的模糊K-Modes初始化算法

模糊-Modes聚类算法针对分类属性的数据进行聚类,使用爬山法来寻找最优解,因此该算法对初始值较为敏感。为了克服该缺点,提出一种动态的模糊K—Modes初始化算法,该方法能够自动确定聚类数目,以及对应的聚类中心;而且能够应用于数值属性和分类属性相混合的数据集。该初始化算法可以有效地克服模糊K—Modes算法对初值的敏感性。实验的结果表明了该初始化算法的可行性和有效性。     

Mining of mixed data with application to catalog marketing

Clustering is one of the most popular techniques in data mining. The goal of clustering is to identify distinct groups in a dataset. Many clustering algorithms have been published so far, but often limited to numeric or categorical data. However, most real world data are mixed, numeric and categorical. In this paper, we propose a clustering algorithm CAVE which is based on variance and entropy, and is capable of mining mixed data. The variance is used to measure the similarity of the numeric part of the data. To express the similarity between categorical values, distance hierarchy has been proposed. Accordingly, the similarity of the categorical part is measured based on entropy weighted by the distances in the hierarchies. A new validity index for evaluating the clustering results has also been proposed. The effectiveness of CAVE is demonstrated by a series of experiments on synthetic and real datasets in comparison with that of several traditional clustering algorithms. An application of mining a mixed dataset for customer segmentation and catalog marketing is also presented.