中药材分类中的聚类分析

本文应用聚类分析中的系统聚类法与模糊聚类法,对植物药材百合的18个样品进行了聚类．结果不仅两法所得的结论相一致,而且与传统分类的情况相符合．     

逐步聚类法及其应用

本文介绍了一种非等级分类方法——逐步聚类法,并将其应用于翅果油树灌丛的数量分类研究,结果表明：逐步聚类法实现最优分类的目标过程,是依样方组内具有最小的离差平方和。样方组间具有最大的离差平方和为标准,使样方组内具有最大的同质性,样方组间具有最大的异质性,其分类结果与实际情况吻合度较高;其次,逐步聚类法只需计算每个样方到该样方形心的距离,可缩短计算时间和节省计算机内存单元,提高工作效率。 与模糊c—均值聚类和TWINSPAN结果相比,逐步聚类的结果类似于模糊c—均值聚类,即样方组内具有较高的同质性;在不要求分类结果具有明显上下级关系的前提下,逐步聚类结果要优于TWINSPAN。     

生物序列的聚类分析

通过不同的聚类方式,对公共数据库中生物序列数据进行生物信息的挖掘,以达到在更广泛和更深入的框架中了解它们之间的相互关系的目的。以帕金森病相关基因所对应的mRNA序列为例,使用双序列比对的得分值作为序列之间的距离定义。同时为解决不同聚类分析之间的差异,分别采用模糊聚类和层次聚类两种不同的方法进行聚类分析。并由不同聚类方法得到的一致分类聚类的结果为基因功能分类提供支持,为进一步揭示生物序列所蕴涵的生物学知识和生物学规律提供可参考的依据。     

针茅草原放牧衰退演替阶段的模糊聚类分析

本文利用模糊聚类分析的系统聚类法和ISODATA分类法,对短花针茅草原因过度放牧而形成的衰退演替系列的各阶段进行了分类;比较成功地在群落分类上联合使用了PCA排序与ISODATA分类。结果表明:这两种方法都将衰退演替系列分成4个阶段,它们是短花针茅群落、扁穗冰草群落、冷蓠群落和亚氏旋花群落。这与前人用其他方法分类的结果相似。本文为群落分类提供了两种合理、可行的数学分类方法。     

粗糙集模糊聚类分析法在昆虫分类研究中的应用

本文根据昆虫图像,对半翅目、鳞翅目、鞘翅目的28种昆虫提取的形状参数、叶状性、球状性等7项数学形态特征进行了粗糙集模糊聚类分析。在粗糙集处理的基础上,分别进行7指标和3指标（相对约简）两种不同的模糊聚类分析法相比较。结果显示,在作为目级阶元分类指标时,各项特征的重要性依次为：(似圆度、偏心率)＞（亮斑数、球状性、圆形性）＞（叶状性、形状参数）;粗糙集分类正确率优于模糊聚类分析法;粗糙集处理后的3指标分类正确率优于未处理的7指标分类正确率。结论认为,粗糙集理论在昆虫依据数学形态特征进行分类方面与统计分析方法相比更有优势,粗糙集滤过指标后再进行模糊聚类法分析在昆虫分类研究上具有重要意义。     

直接模糊聚类的截取水平选择及其在植被数量分析中的应用

植被生态研究中常用的聚类法,是着眼于研究区域中植被和环境因子呈间断分布或变化梯度较大的一类情况,对原始数据中各个体按其属性进行归类。直接模糊聚类法则以各个体间的属性相近程度来定义一模糊关系矩阵,然后对矩阵取不同的水平截集,从而得出一等级分类。当模糊关系确定以后,截取水平的选择就成了聚类结果的决定性因素。至目前为止,直接模糊聚类中的截取水平通常由分析者主观给定,或者是以逐步试验,逐步修改的方法确定的。这样,聚类结果就不可避免地带有较大的主观和任意性。笔者认为截取水平应选在模糊关系变化较大之处,使聚类结果尽可能地反映原始数据的结构特征。这一原理已被实施于一通用软件中,实例分析表明,如此选择的截取水平确能比较客观地反映原始数据的特征,从而得出较为合理的聚类结果。     

模糊聚类分析法在昆虫杆状病毒分类中的应用

采用一种新的数学方法——模糊聚类分析法,对已知包涵体蛋白N-末端序列的10种昆虫杆状病毒进行分类,其结果与Fitch方法的结果完全一致。这种分类方法并不只是对各序列进行简单的类别划分,而且也表示了各类间相似的程度及在某一类内各序列间的进化关系。     

蛋白质序列的聚类结构分析

基于模糊邻近关系的粒度空间,对蛋白质序列进行聚类结构分析。利用MEGA软件计算选取的木聚糖酶序列间的比对距离,引入内积将其转化为模糊邻近关系(或矩阵),再应用算法求解其粒度空间,进行序列的聚类结构分析和最佳聚类确定研究。这些研究为蛋白质序列提供了定量分析的工具。     

小麦抗旱生态分类中适合性聚类方法的研究

探索了适合于小麦品种抗旱生态分类的聚类方法。选用２１个农艺性状和１５个冬小麦品种（系）,在聚类分析的各环节上,通过采用不同的策略,大规模进行了各种分类结果的比较。结果表明,在与专家经验分类接近程度上,数据转换方法中,原始数据法依次大于普通相关阵基础上的方差极大正交旋转法、Ｐｒｏｍａｘ斜交旋转法、主成份法;相似性度量上,欧氏距离大于马氏距离;聚类方式上,对应分析法和模糊聚类法大于最短距离法、最长距离     

模糊聚类分析法及其在群落聚类分析中的应用

以不同种类蔬菜菜地节肢动物群落作为研究对象,介绍模糊聚类分析法在群落相似性分析中的应用。得到如下的结果：在建立模糊等价关系的基础上,根据不同的需要,当λ取０．８９９时,聚合成３个类群,当λ为０．８５２时,聚合成２个类群。而Ｆ－ＰＦＳ聚类分析法则根据群落在聚类类群之间或其中的散布情况,从所有的聚类结果中,选择最佳的聚类方案。本文材料的最佳的聚类结果为：把群落聚合成２类。类群１包括：菜心、小白菜、奶白菜、芥菜、芥兰、通菜、豇豆、四季豆上的节肢动物群落;类群２包括：藤菜与苋菜上节肢动物群落。     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

模糊图论分类的截取水平选择

在数量分类中,模糊图论的最大树方法可直接依据模糊相似系数得到树状图。但至目前为止,模糊图论分类中的截取水平通常由分析者主观给定,或者是以逐步试验、逐步修改的方法确定的。用太白山针叶林的数量分类研究结果表明,模糊图论分类的截取水平应选在模糊关系变化较大处,并可用数学方法确定。研究的植被分类实例结果与模糊聚类分析是一致的。     

The management of vegetation classifications with fuzzy clustering

Questions: Does fuzzy clustering provide an appropriate numerical framework to manage vegetation classifications? What is the best fuzzy clustering method to achieve this? Material: We used 531 relevés from Catalonia (Spain), belonging to two syntaxonomic alliances of mesophytic and xerophytic montane pastures, and originally classified by experts into nine and 13 associations, respectively. Methods: We compared the performance of fuzzy C‐means (FCM), noise clustering (NC) and possibilistic C‐means (PCM) on four different management tasks: (1) assigning new relevé data to existing types; (2) updating types incorporating new data; (3) defining new types with unclassified relevés; and (4) reviewing traditional vegetation classifications. Results: As fuzzy classifiers, FCM fails to indicate when a given relevé does not belong to any of the existing types; NC might leave too many relevés unclassified; and PCM membership values cannot be compared. As unsupervised clustering methods, FCM is more sensitive than NC to transitional relevés and therefore produces fuzzier classifications. PCM looks for dense regions in the space of species composition, but these are scarce when vegetation data contain many transitional relevés. Conclusions: All three models have advantages and disadvantages, although the NC model may be a good compromise between the restricted FCM model and the robust but impractical PCM model. In our opinion, fuzzy clustering might provide a suitable framework to manage vegetation classifications using a consistent operational definition of vegetation type. Regardless of the framework chosen, national/regional vegetation classification panels should promote methodological standards for classification practices with numerical tools.     

Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process

Abstract

To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model’s adaptive abilities in various operation conditions and can improve its generalization ability.     

Fuzzy Kernel Clustering of RNA Secondary Structure Ensemble Using a Novel Similarity Metric

Abstract

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.     

Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition

In this paper we present a study of classification of the 20 amino acids via a fuzzy clustering technique. In order to calculate distances among the various elements we employ two different distance functions: the Minkowski distance function and the NTV metric. In the clustering procedure we take into account several physical properties of the amino acids. We examine the effect of the number and nature of properties taken into account to the clustering procedure as a function of the degree of similarity and the distance function used. It turns out that one should use the properties that determine in the more important way the behavior of the amino acids and that the use of the appropriate metric can help in defining the separation into groups.     

Fuzzy methods for the detection of copy number variations in comparative genomic hybridization arrays

Genomic copy number variations (CNVs) are considered as a significant source of genetic diversity and widely involved in gene expression and regulatory mechanism, genetic disorders and disease risk, susceptibility to certain diseases and conditions, and resistance to medical drugs. Many studies have targeted the identification, profiling, analysis, and associations of genetic CNVs. We propose herein two new fuzzy methods, taht is, one based on the fuzzy inference from the pre-processed input, and another based on fuzzy C-means clustering. Our solutions present a higher true positive rate and a lower false negative with no false positive, efficient performance and consumption of least resources.