A tabu search-based algorithm for the fuzzy clustering problem

The Fuzzy Clustering Problem (FCP) is a mathematical program which is difficult to solve since it is nonconvex, which implies possession of many local minima. The fuzzy C-means heuristic is the widely known approach to this problem, but it is guaranteed only to yield local minima. In this paper, we propose a new approach to this problem which is based on tabu search technique, and aims at finding a global solution of FCP. We compare the performance of the algorithm with the fuzzy C-means algorithm.     

A heuristic clustering algorithm using union of overlapping pattern-cells

Relative geometric arrangements of the sample points, with reference to the structure of the imbedding space, produce clusters. Hence, if each sample point is imagined to acquire a volume of a small M-cube (called pattern-cell), depending on the ranges of its (M) features and number (N) of samples; then overlapping pattern-cells would indicate naturally closer sample-points. A chain or blob of such overlapping cells would mean a cluster and separate clusters would not share a common pattern-cell between them. The conditions and an analytic method to find such an overlap are developed. A simple, intuitive, nonparametric clustering procedure, based on such overlapping pattern-cells is presented. It may be classified as an agglomerative, hierarchical, linkage-type clustering procedure. The algorithm is fast, requires low storage and can identify irregular clusters. Two extensions of the algorithm, to separate overlapping clusters and to estimate the nature of pattern distributions in the sample space, are also indicated.     

A multipopulation cultural algorithm using fuzzy clustering

In this paper, we propose a new model for multimodal function optimization, based on the concepts of cultural algorithms and fuzzy clustering. Fuzzy clustering is used in order to partition a same population into communicating subpopulations which evolve in parallel, whilst cultural exchange ensures preserving population diversity. The ability of this model to identify global/local optima without loss of diversity is demonstrated using several multimodal test functions, including a deceptive example.     

A trend based investment decision approach using clustering and heuristic algorithm

This paper proposes a stock price trend clustering and trend investment decision model by using a genetic algorithm to search for optimal solutions and the best investment strategies for different stock price trends.The new price trend clustering model identifies three types of stock price movements:uptrends,sideways trends,and downtrends.Unfortunately,trends discovered through stock price movements or technical indicator graphs are typically subjective and unquantifiable.This paper takes daily stock prices and trading volume data from the China Shanghai Stock Exchange Composite Index(SSECI)from January 2,1997 to August31,2012,to examine the performance of the proposed trend clustering model.The proposed model is also compared to other popular stock market investment strategies to verify its validity.Research result shows that the proposed trend clustering model correctly identifies three different trends in the stock market.Furthermore,the trend investment strategy model developed by using genetic algorithm methodology performs better than other investment strategies,namely,Granville’s rule,the KD indicator strategy,the buys and holds strategy,and GMA rules,in both bull and bear market periods.Research results prove the proposed new model to be a stable and valid investment strategy.     

A parallel fuzzy clustering algorithm for large graphs using Pregel

Large graphs are scale free and ubiquitous having irregular relationships. Clustering is used to find existent similar patterns in graphs and thus help in getting useful insights. In real-world, nodes may belong to more than one cluster thus, it is essential to analyze fuzzy cluster membership of nodes. Traditional centralized fuzzy clustering algorithms incur high communication cost and produce poor quality of clusters when used for large graphs. Thus, scalable solutions are obligatory to handle huge amount of data in less computational time with minimum disk access. In this paper, we proposed a parallel fuzzy clustering algorithm named ‘PGFC’ for handling scalable graph data. It will be advantageous from the viewpoint of expert systems to develop a clustering algorithm that can assure scalability along with better quality of clusters for handling large graphs.The algorithm is parallelized using bulk synchronous parallel (BSP) based Pregel model. The cluster centers are initialized using degree centrality measure, resulting in lesser number of iterations. The performance of PGFC is compared with other state of art clustering algorithms using synthetic graphs and real world networks. The experimental results reveal that the proposed PGFC scales up linearly to handle large graphs and produces better quality of clusters when compared to other graph clustering counterparts.     

面向主动学习的模糊核聚类采样算法

针对主动学习中构造初始分类器难以选取代表性样本的问题,提出一种模糊核聚类采样算法。该算法首先通过聚类分析技术将样本集划分,然后分别在类簇中心和类簇边界区域选取样本进行标注,最后依此构造初始分类器。在该算法中,通过高斯核函数把原始样本空间中的点非线性变换到高维特征空间,以达到线性可聚的目的,并引入了一种基于局部密度的初始聚类中心选择方法,从而改善聚类效果。为了提高采样质量,结合划分后各类簇的样本个数设计了一种采样比例分配策略。同时,在采样结束阶段设计了一种后补采样策略,以确保采样个数达标。实验结果分析表明,所提算法可以有效地减少构造初始分类器所需的人工标注负担,并取得较高的分类正确率。     

创意FCM算法

针对现有模糊聚类方法仅仅是对已有数据点的聚类的不足,提出了在已有数据集的基础上找到新的一类集群的聚类方法 CFCM。该算法在FCM算法的基础上,通过引入观测点P作为聚类的先验知识,来大致确定未知集群的聚类中心,定义了权重系数λ来限定观测点对新的一类聚类中心形成的影响程度。人造数据集和UCI真实数据集的实验结果表明,该算法不仅对已知数据点有较好的聚类效果,并且可以在观测点P的作用下在特定区域创造出新的一类无已知数据点的集群中心点的大致位置,因而在实际中有潜在应用价值。     

基于密度的模糊代表点聚类算法

结合密度聚类和模糊聚类的特点,提出一种基于密度的模糊代表点聚类算法.首先利用密度对数据点成为候选聚类中心点的可能性进行处理,密度越高的点成为聚类中心点的可能性越大;然后利用模糊方法对聚类中心点进行确定;最后通过合并聚类中心点确定最终的聚类中心.所提出算法具有很好的自适应性,能够处理不同形状的聚类问题,无需提前规定聚类个数,能够自动确定真实存在的聚类中心点,可解释性好.通过结合不同聚类方法的优点,最终实现对数据的有效划分.此外,所提出的算法对于聚类数和初始化、处理不同形状的聚类问题以及应对异常值等方面具有较好的鲁棒性.通过在人工数据集和UCI真实数据集上进行实验,表明所提出算法具有较好的聚类性能和广泛的适用性.     

利用空间信息的核模糊C均值聚类算法

模糊聚类,特别是模糊C均值聚类算法（FCM）广泛地运用到图像的分割中。但是传统的算法未对数据对特征进行优化,亦未考虑图像的空间信息,对噪声图像分割不理想。在FCM目标函数中引入核函数,用内核引导距离代替传统的欧式距离,同时考虑到邻近象素的影响,增加了空间约束项,提出了利用空间信息的核FCM算法。通过对模拟图和仿真脑部MR图像的分割实验证明,该算法可以有效的分割含有噪声图像。     

A heuristic error-feedback learning algorithm for fuzzy modeling

Describes a type of fuzzy system with interpolating capability to extract MISO fuzzy rules from input-output sample data through learning. The proposed model inherits many merits from Sugeno-type models and their variations. A heuristic error-feedback learning algorithm associated with the model is suggested. Based on which, the estimator is shown to have a self-adjusting step when approaching a minimum     

共有信息引导的启发式聚类算法

启发式聚类算法采用局部搜索策略发现使得目标函数取极小值的聚类结果,即局部最优聚类结果。算法虽然具有收敛速度快等优点,但是初始解敏感问题严重地影响了聚类结果的质量。利用多个局部最优聚类结果中的共有信息设计启发式聚类算法。首先给出共有信息的定义及其发现算法FCI_G;然后利用共有信息设计启发式聚类算法CIGC;最后在多组仿真和实际数据集上考察了CIGC算法的性能。实验结果表明,共有信息对提高聚类算法质量有着显著的作用。     

一种改进的模糊聚类算法

传统模糊聚类算法如模糊C-均值(FCM)算法中,用户必须预先指定聚类类别数C,且目标函数收敛速度过慢。为此,将粒度分析原理应用在FCM算法中,提出了基于粒度原理确定聚类类别数的方法,并采用密度函数法初始化聚类中心。实验结果表明,改进后的聚类算法能够得到合理有效的聚类数目,并且与随机初始化相比,迭代次数明显减少,收敛速度明显加快。     

A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm

In this paper, we show how one can take advantage of the stability and effectiveness of object data clustering algorithms when the data to be clustered are available in the form of mutual numerical relationships between pairs of objects. More precisely, we propose a new fuzzy relational algorithm, based on the popular fuzzy C-means (FCM) algorithm, which does not require any particular restriction on the relation matrix. We describe the application of the algorithm to four real and four synthetic data sets, and show that our algorithm performs better than well-known fuzzy relational clustering algorithms on all these sets.     

A spatially constrained fuzzy hyper-prototype clustering algorithm

We present in this paper a fuzzy clustering algorithm which can handle spatially constraint problems often encountered in pattern recognition. The proposed method is based on the notions of hyperplanes, the fuzzy c-means, and spatial constraints. By adding a spatial regularizer into the fuzzy hyperplane-based objective function, the proposed method can take into account additionally important information of inherently spatial data. Experimental results have demonstrated that the proposed algorithm achieves superior results to some other popular fuzzy clustering models, and has potential for cluster analysis in spatial domain.     

广义多变量模糊C均值聚类算法

模糊聚类算法为了保证算法的收敛性,要求模糊指标m取值大于1,这限制了算法的普适性。提出广义多变量模糊C均值聚类算法(GMFCM),在多变量模糊C均值聚类算法(MFCM)的基础上,利用粒子群优化算法对分量模糊隶属度进行优化估计,进而将模糊指标拓展到m>0的情况,同时采用梯度法得到算法聚类中心迭代公式。GMFCM理论分析了模糊指标m扩展的原理,研究了模糊指标m在不同取值情况下的性质,解释了模糊指标m的实际意义,讨论了GMFCM算法的收敛性。GMFCM继承了MFCM算法的样本分量区分性能,弥补了MFCM算法聚类中心分量与样本分量重合时的不完备性,突破了模糊聚类算法对参数m的约束,提高了模糊聚类算法的普适性。基于gauss数据集和UCI数据集的仿真测试验证了所提算法的有效性。     

A scatter search-based heuristic to locate capacitated transshipment points

We consider a fixed charge two-stage location problem in which a given number of intermediate transshipment points are to be located between the supply plants and the customer locations. Both plants and transshipment points are capacitated. Scatter search is a population-based heuristic that has been applied to several combinatorial optimization problems. We develop an efficient scatter search-based heuristic approach with hybrid improvements including local search and path-relinking routines. Computational results demonstrate the effectiveness of the heuristic even for realistic problems with larger instances and tighter capacities.     

Video segmentation using a histogram-based fuzzy c-means clustering algorithm

The purpose of video segmentation is to segment video sequence into shots where each shot represents a sequence of frames having the same contents, and then select key frames from each shot for indexing. Existing video segmentation methods can be classified into two groups: the shot change detection (SCD) approach for which thresholds have to be pre-assigned, and the clustering approach for which a prior knowledge of the number of clusters is required. In this paper, we propose a video segmentation method using a histogram-based fuzzy c-means (HBFCM) clustering algorithm. This algorithm is a hybrid of the two approaches aforementioned, and is designed to overcome the drawbacks of both approaches. The HBFCM clustering algorithm is composed of three phases: the feature extraction phase, the clustering phase, and the key-frame selection phase. In the first phase, differences between color histogram are extracted as features. In the second phase, the fuzzy c-means (FCM) is used to group features into three clusters: the shot change (SC) cluster, the suspected shot change (SSC) cluster, and the no shot change (NSC) cluster. In the last phase, shot change frames are identified from the SC and the SSC, and then used to segment video sequences into shots. Finally, key frames are selected from each shot. Simulation results indicate that the HBFCM clustering algorithm is robust and applicable to various types of video sequences.     

改进的遗传模糊聚类算法对医学图像的分割

利用遗传算法全局随机搜索的特点,可以解决模糊C均值聚类（FCM）算法在医学图像分割中容易陷入局部最优解的问题,但确定遗传算法的初始搜索范围时,需要借助于人的经验。为此,用收敛速度快的硬聚类算法得到的聚类中心作为参考,上下浮动划出一个较小的数据范围,作为遗传算法的初始搜索空间。该方法在避免FCM算法陷入局部最优化的同时,也加速了遗传算法的收敛过程。实验表明,该方法相对于标准的遗传模糊算法,效果要好得多。     

基于模糊聚类的模糊神经网络控制

对于复杂的非线性离散系统,提出将模糊聚类算法同神经网络相结合,使用衡量聚类有效性的S函数确定模糊规则数目,进而确定模糊神经网络的结构;控制器的设计应用LMI方法。以典型的非线性系统二级倒立摆为例,在Matlab中进行仿真实验,结果表明,基于聚类算法的神经网络控制能够在较大范围的初始状态下使系统获得稳定。     

A novel background updation algorithm using fuzzy c-means clustering for pedestrian detection

Multimedia Tools and Applications - The task of pedestrian detection in video surveillance applications will face challenges like dynamic background changes, false human detection (shadow), and...