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。     

模糊图论在山西植被区划中的应用

本文用模糊图论的最大树方法对山西植被区划进行了数量分类研究,将山西植被分为2个植被地带,6个植被地区,17个植被小区。与在植物群落学中应用的模糊聚类分析和其它方法相比,模糊图论的最大树方法直接依模糊相似系数矩阵得到树状图,从而避免了相似系数矩阵复杂的合成运算,而分类结果不仅更具直观性,而且也是令人满意的。我们认为模糊图论应用于植被区划是合适的。     

太白山针叶林的Fuzzy分类研究

应用模糊聚类分析法和模糊图论分析对太白山针叶林进行了数量分类比较研究。将２６个样地分为两大类共７个群落类型。研究结果表明,两种方法在植物群落分类研究中,不但是可行的,而且所分类的实际结果是等价的,与实际观测情况也是吻合的。其中的图论法直接依据模糊相似系数得到树状图,简便易行,显示出更大的适用性。     

关于植物群丛划分的探讨

在传统的植物群落分类系统中,群丛是植物群落分类的基本单位.从群丛分类的必要性出发,综述了传统植物群落分类系统中对群丛的定义及其划分方法,即在群丛的划分中主要依据群落中不同层片的优势种或特征种;但是在利用传统植物群落分类方法划分群丛时也存在一些不确定性因素,主要表现在确定群丛的特征种(组)时需要人为确定;同时,论述了当前植物群落数量分类的研究现状,分析了利用双向指示种分析法(TWINSPAN)、主成分分析(PCA)等数量分类方法划分群丛时存在的一些问题,主要表现在数量分类结果与传统分类单位的对应关系不能达到协调一致,无法判断是否划分到了群丛的水平.最后提出了群丛划分方法的展望:数量方法是基础,特征种(组)是及其数量特征是关键.     

模糊ＩＳＯＤＡＴＡ在草地植物群落分类上的应用

 模糊集合理论在元素与集合的关系中引入了连续变化的隶属度,因而能适应植物群落作连续和明显间断分布同时存在的情况。本文运用以模糊集合理论为基础的ISODATA聚类分析方法对松嫩平原草地植物群落进行了数量分类,结果说明模糊ISODATA聚类比传统的分类方法有更为客观、灵活的特点,是一种较为理想的植被数量分类方法。     

植酸酶的多样性及其分类

植酸酶是一类催化植酸水解逐步释放磷酸基团形成低级肌醇磷酸衍生物的正磷酸单酯磷酸水解酶。植酸酶在动物营养、资源环境保护和人类健康等领域有巨大的应用潜力。目前,人们对植酸酶的多样性及其分类的认识比较模糊甚至错误,严重影响了植酸酶的研究进程和水平。首先简要概述了基于最适pH和立体专一性的植酸酶分类,然后着重论述了基于结构和催化机理的植酸酶分类及其代表酶特征的最新研究进展,最后探讨了根据不同分类标准特别是基于结构和催化机理准确理解和全面表征各种植酸酶的重要性,以期为植酸酶的研究和应用提供参考。     

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。     

中国地方品种鸡的分类研究

中国鸡种资源极为丰富,中国历史上形成的许多优良地方品种鸡,对中国养鸡事业发展和世界养鸡业都曾做出过重要贡献。为充分利用我国地方鸡种资源遗传潜力和杂种优势,本文利用图论主成分分类法与系统聚类法根据20项指标研究了中国30个地方品种鸡的分类问题,分类结果比较符合实际,特别是图论主成分分类图直观清楚,生物学含意明确,对中国鸡种资源和基因库的利用有实际参考价值。     

瑞典河漫滩草甸植被的数量分类和排序

本文用目前各国学者广泛使用的一些数量分类和排序方法对瑞典河漫滩草甸样地资料进行了分类和排序。所用的方法包括多元等级聚合分类(TABORD程序),多元等级分划分类(TWINSPAN程序),PCA排序(ORDINA程序),RA和DCA排序(DECORANA程序)。研究结果表明可以把28个样地分为6个群落类型,它们的分布格局是与土壤水分梯度密切相关的。此外本文还对数量分类和排序方法在植物群落学研究中的应用以及所用方法的比较进行了讨论。     

中国乌头属属下等级数量分类的初步研究

本文取中国乌头属50种(含变种)植物,选取形态学性状46个、化学性状8个、组织学性状5个、生态学性状1个,运用数量分类方法,对该属进行属下等级的分类研究。根据数学分析结果,确定了树系图中变种、种、系、亚属的分级界线,并在聚类分析的基础上,进一步探讨了各等级类群的划分。     

复方配伍的摄动模糊聚类方法

利用摄动模糊聚类方法对中药复方桂枝汤的药群进行了分类,结果表明该方法优于传统模糊聚类方法,避免了利用传递闭包求模糊等价矩阵进行分类的失真问题,与中药传统组方原则相吻合。     

Using genetic algorithms for the construction of phylogenetic trees: application to G-protein coupled receptor sequences

Many different phylogenetic clustering techniques are used currently. One approach is to first determine the topology with a common clustering method and then calculate the branch lengths of the tree. If the resulting tree is not optimal exchanging tree branches can make some local changes in the tree topology. The whole process can be iterated until a satisfactory result has been obtained. The efficiency of this method fully depends on the initially generated tree. Although local changes are made, the optimal tree will never be found if the initial tree is poorly chosen. In this article, genetic algorithms are applied such that the optimal tree can be found even with a bad initial tree topology. This tree generating method is tested by comparing its results with the results of the FITCH program in the PHYLIP software package. Two simulated data sets and a real data set are used.     

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.     

Selection of informative clusters from hierarchical cluster tree with gene classes

Background  

A common clustering method in the analysis of gene expression data has been hierarchical clustering. Usually the analysis involves selection of clusters by cutting the tree at a suitable level and/or analysis of a sorted gene list that is obtained with the tree. Cutting of the hierarchical tree requires the selection of a suitable level and it results in the loss of information on the other level. Sorted gene lists depend on the sorting method of the joined clusters. Author proposes that the clusters should be selected using the gene classifications.     

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.     

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。     

Hierarchical tree snipping: clustering guided by prior knowledge

MOTIVATION: Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence. RESULTS: To improve the biological significance of the clustering, we develop a new framework of partitioning by snipping--cutting selected edges at variable levels. The snipped edges are selected to induce clusters that are maximally consistent with partially available background knowledge such as functional classifications. Algorithms for two key applications are presented: functional prediction of genes, and discovery of functionally enriched clusters of co-expressed genes. Simulation results and cross-validation tests indicate that the algorithms perform well even when the actual number of clusters differs considerably from the requested number. Performance is improved compared with a previously proposed algorithm. AVAILABILITY: A java package is available at http://www.cs.bgu.ac.il/~dotna/ TreeSnipping