Pair-copula based mixture models and their application in clustering

Finite mixtures are often used to perform model based clustering of multivariate data sets. In real life applications, such data may exhibit complex nonlinear form of dependence among the variables. Also, the individual variables (margins) may follow different families of distributions. Most of the existing mixture models are unable to accommodate these two aspects of the data. This paper presents a finite mixture model that involves a pair-copula based construction of a multivariate distribution. Such a model de-couples the margins and the dependence structures. Hence, the margins can be modeled using different families. Again, many possible dependence structures can also be studied using different copulas. The resulting mixture model (called DVMM) is then capable of capturing a broad family of distributions including non-Gaussian models. Here we study DVMM in the context of clustering of multivariate data. We design an expectation maximization procedure for estimating the mixture parameters. We perform extensive experiments on the basis of a number of well-known data sets. A detailed evaluation of the clustering quality obtained by DVMM in comparison to other mixture models is presented. The experimental results show that the performance of DVMM is quite satisfactory.     

Centroid index: Cluster level similarity measure

In clustering algorithm, one of the main challenges is to solve the global allocation of the clusters instead of just local tuning of the partition borders. Despite this, all external cluster validity indexes calculate only point-level differences of two partitions without any direct information about how similar their cluster-level structures are. In this paper, we introduce a cluster level index called centroid index. The measure is intuitive, simple to implement, fast to compute and applicable in case of model mismatch as well. To a certain extent, we expect it to generalize other clustering models beyond the centroid-based k-means as well.     

K-means: Clustering by gradual data transformation

Traditional approach to clustering is to fit a model (partition or prototypes) for the given data. We propose a completely opposite approach by fitting the data into a given clustering model that is optimal for similar pathological data of equal size and dimensions. We then perform inverse transform from this pathological data back to the original data while refining the optimal clustering structure during the process. The key idea is that we do not need to find optimal global allocation of the prototypes. Instead, we only need to perform local fine-tuning of the clustering prototypes during the transformation in order to preserve the already optimal clustering structure.     

Hybrid clustering solution selection strategy

Cluster ensemble approaches make use of a set of clustering solutions which are derived from different data sources to gain a more comprehensive and significant clustering result over conventional single clustering approaches. Unfortunately, not all the clustering solutions in the ensemble contribute to the final result. In this paper, we focus on the clustering solution selection strategy in the cluster ensemble, and propose to view clustering solutions as features such that suitable feature selection techniques can be used to perform clustering solution selection. Furthermore, a hybrid clustering solution selection strategy (HCSS) is designed based on a proposed weighting function, which combines several feature selection techniques for the refinement of clustering solutions in the ensemble. Finally, a new measure is designed to evaluate the effectiveness of clustering solution selection strategies. The experimental results on both UCI machine learning datasets and cancer gene expression profiles demonstrate that HCSS works well on most of the datasets, obtains more desirable final results, and outperforms most of the state-of-the-art clustering solution selection strategies.     

基于多尺寸网格的LEACH协议改进

针对低功耗自适应集簇分层型(LEACH)协议中节点采集的数据存在大量冗余和能量消耗不均衡问题,提出一种能量高效路由协议MDG-LEACH。该协议基于虚拟网格和多尺寸网格选取活跃节点,采用综合考虑节点剩余能量和空间分布情况的簇头选择机制,簇头与基站之间根据动态规划方法建立最短传输路由树进行数据传输。仿真结果表明,与LEACH协议以及单剑锋的LEACH改进协议(计算机技术与发展,2013年第2期)相比,MDGLEACH协议在均衡节点能量消耗和延长无线传感器网络寿命方面有了较大提高。     

FO-CA:一种基于距离差异度组合权重的多属性数据分类方法

为了解决多属性数据分类问题,提出了一种基于模糊优选模型与聚类分析的分类方法(FO-CA)。首先由模糊优选模型得到有序综合指标数据集,其中在权重阶段提出了距离差异度并以此为依据构建了一种组合主客观权重的赋权方法;然后采用聚类分析将有序综合指标数据集聚类为几个簇进而分类;最后选取UCI中的Iris、Wine和Ruspini 3个数据集进行仿真实验。实验结果表明,该分类方法相比模糊优选方法及K-Means算法能获得更好的分类结果,对决策者有一定的参考价值。     

基于网格的量子博弈聚类算法

量子博弈是对经典博弈的量子模拟,利用量子的纠缠态,可以使博弈参与人在博弈策略的选择过程中相互影响,从而得到与经典博弈不同的结果。将量子博弈运用于聚类问题,并提出一种基于网格的量子博弈聚类算法。算法将数据点看作是博弈的参与人,通过在收益矩阵中内嵌距离函数,使相似的数据点能够获得更大的收益,从而形成聚类。此外,通过设定网格合并规则,使博弈过程得到了简化。仿真实验表明,算法在聚类质量上优于传统的K-means等算法。最后,就算法中的几个参数对算法性能的影响进行了讨论,并给出了参数选择的建议。     

聚类方法综述

文中对聚类方法作了综述。系统地讨论了聚类研究中的4个关键内容:数据点之间"距离"函数的定义方法、类数目的确定、高效优良的聚类算法和聚类算法好坏的评估。分析了各类聚类算法的优劣性,指出了聚类分析研究的发展趋势。     

云环境下基于Canopy聚类的FCM算法研究

FCM算法是目前广泛使用的算法之一。,针对FCM聚类质量和收敛速度依赖于初始聚类中心的问题,结合Canopy聚类算法能够粗略快速地对数据集进行聚类的优点,提出了一种基于Canopy聚类的FCM算法。该算法通过将Canopy算法快速获取到的聚类中心作为FCM算法的输入来加快FCM算法收敛速度。并在云环境下设计了其MapReduce化方案,实验结果表明,MapReduce化的基于Canopy聚类的FCM算法比MapReduce化的FCM聚类算法具有更好的聚类质量和运行速度。     

面向大规模数据的分层近邻传播聚类算法

近邻传播(Affinity Propagation,AP)聚类具有不需要设定聚类个数、快速准确的优点,但无法适应于大规模数据的应用需求。针对此问题,提出了分层近邻传播聚类算法。首先,将待聚类数据集划分为若干适合AP算法高效执行的子集,分别推举出各个子集的聚类中心;然后对所有子集聚类中心再次执行AP聚类,推举出整个数据集的全局聚类中心;最后根据与这些全局聚类中心的相似度对聚类样本进行划分,从而实现对大规模数据的高效聚类。在真实和模拟数据集上的实验结果均表明,与AP聚类和自适应AP聚类相比,该方法在保证较好聚类效果的同时,极大地降低了聚类的时间消耗。