Geometric clustering to minimize the sum of cluster sizes

1.1 如果是Matlab安装光盘上的工具箱，重新执行安装程序，选中即可；

1.2 如果是单独下载的工具箱，一般情况下仅需要把新的工具箱解压到某个目录。

2 在matlab的file下面的set path把它加上。

3 把路径加进去后在file→Preferences→General的Toolbox Path Caching里点击update

Toolbox Path Cache更新一下。

4 用which newtoolbox_command.m来检验是否可以访问。如果能够显示新设置的路径，则表明该工具箱可以使用了。

把你的工具箱文件夹放到安装目录中“toolbox”文件夹中，然后单击“file”菜单中的“setpath”命令，打开“setpath”对话框，单击左边的“ADDFolder”命令，然后选择你的那个文件夹，最后单击“SAVE”命令就OK了。

MATLAB Toolboxes

＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝

• ADCPtools - acoustic doppler current profiler data processing

• AFDesign - designing analog and digital filters

• AIRES - automatic integration of reusable embedded software

• Air-Sea - air-sea flux estimates in oceanography

• Animation - developing scientific animations

• ARfit - estimation of parameters and eigenmodes of multivariate autoregressive methods

• ARMASA - power spectrum estimation

北京工商大学

模式识别作业汇总

（2014年秋季学期）

课程名称：模式识别

专业班级：计研141班

学生姓名：董文菲 刘倩

指导教师：于重重

成绩：

2015年1月20日

第一次课的作业

1. 在Matlab 环境下，利用第一题中给了matlab程序，尝试声音识别过程，并把程序流程图画出。

解：程序实现了识别“kiss”与“love”两个声音的界面图。程序流程图如下：

开始设置程序运行路径加载imbook.mat文件，导入参考数据作名为“Pattern

Recognition

DEMO”的弹框，并设置属性及列表值Visit the web

siteStart Pattern

Recognition DemoContact Author声音识别结束

2. 运行网址 http://www.wcl.ece.upatras.gr/en/ai/resources/demo-emotion-recognition-from-speech

上的java applet ，了解声音识别的过程。

解：智能对话系统的测试点如下图所示，

该智能对话系统可以通过语音交互获取信息，控制娱乐设备等智能应用。情感识别的构成如下图：

情感识别依赖于先进的音频参数化技术，利用高斯混合模型训练情绪识别模型。目前负面情绪的识别得到了很好的结果。

3. 选择鸢尾花数据集（iris，网上下载），并尽可能多地使用《数据挖掘导论第三章》介绍的不同的可视化技术完成数据预处理（可参看第三章资料中的辅助ppt），形成报告。文献注释和该书网站提供了可视化软件的线索。

解：（1）分类

选择朴素贝叶斯分类器算法，训练选项选择交叉验证，即把数据集分成若干份，1份作为验证集，其余部分作为训练集合。这样的方法保证了数据集的所有元素都被验证过。这里把数据集分为10份来进行训练。

北京信息工程学院毕业论文（设计）用纸 第 1 页

第一章 Microarray 介绍

1.1 生物信息处理

基于对生物体“硬件”和“软件”的认识 ,提出暂时地撇开生物的物理属性 ,着重研究其信息属性 ,从而进入到生物信息处理 (关于生命硬件的信息和软件的信息 ,即生理信息和生命信息 )的一个分支 ,生物信息学。于是 ,为揭开生命之秘、揭示与生命现象相关的复杂系统的运作机制打开一条新的途径。

什么是生物信息处理

生物信息处理的英文是Ｂｉｏｉｎｆｏｒｍａｔｉｃｓ。 1994年初 ,诺贝尔医学奖获得者美国教授Ｍ·罗德贝尔发表一篇评论 ,题为《生物信息处理 :评估环境卫生的新方法》。他认为生物信息处理是在基因数据库基础上 ,计算机驱动的能快速获得表达基因部分序列的方法。通过ＭＥＤＬＩＮＥ数据库 ,可以查阅到很多与生物信息处理 (Ｂｉｏｉｎｆｏｒｍａｔｉｃｓ)有关的记录，其中Ｊ Ｆ Ａｉｔｏｎ认为生物信息处理是基于计算机的数据库和信息服务;Ｒ Ｐ Ｍｕｒｒａｙ认为生物信息处理包括两方面：第一是大量现存数据的自动化处理 ,第二是新的信息资源的生成；Ｄ Ｂｅｎｔｏｎ在题为《生物信息处理———一个新的多学科工具的原理和潜力》的文章中说 ,生物信息处理的材料是生物学数据 ,其方法来自广泛的各种各样的计算机技术。 其方法来自广泛的各种各样的计算机技术。近年来 ,生物学数据在爆炸式增长 ,新的计算机方法在不断产生。这些方法在结构生物学、遗传学、结构化药品和分子演变学中是研究工作进展的基础。如果生物医学工程要在各个领域都从研究进展中获取最大好处 ,那么生物学数据健全的基础设施的开发与维护是同等重要的。尽管生物信息处理已经作出重要贡献 ,但是在它成熟时就会面临更大的需求在爆炸式增长 ,新的计算机方法在不断产生。这些方法在结构生物学、遗传学、结构化药品和分子演变学中是研究工作进展的基础。如果生物医学工程要在各个领域都从研究进展中获取最大好处 ,那么生物学数据健全的基础设施的开发与维护是同等重要的。尽管生物信息处理已经作出重要贡献 ,但是在它成熟时就会面临更大的需求。 北京信息工程学院毕业论文（设计）用纸 第 2 页

1 Brain Cancer Microarray Data Weighted Gene Co-expression Network Analysis R Tutorial Steve Horvath, Bin Zhang, Jun Dong, Tova Fuller, Peter Langfelder Correspondence: shorvath@, /biostat/people/horvath.htm This R tutorial describes how to carry out a gene co-expression network analysis with the R software. Content of this tutorial 1) 1) Gene Co-expression Network Construction. We show how to construct unweighted networks using hard thresholding and how to construct weighted networks using soft thresholding. We describe a criterion (scale free topology criterion) for choosing the threshold. 2) Module Definition Based on Average Linkage hierarchical clustering with a tree cutting algorithm 3) Relating modules to prognostic significance for cancer survival time Keywords: gene significance, module significance 4) Relating gene significance to intramodular connectivity. 5) Generalizing intramodular connectivity to all genes on the array. Keyword: module eigengene based connectivity measure 6) Comparing weighted network results to unweighted network results 7) Studying the relationship between the clustering coefficicient and intramodular connectivity. To cite this tutorial or the statistical methods please use the following 2 references 1. Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17 Technical Report and software code at: /labs/horvath/CoexpressionNetwork. 2. Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006) "Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target", PNAS | November 14, 2006 | vol. 103 | no. 46 | 17402-17407 The following theoretical reference explores the meaning of coexpression network analysis • Horvath S, Dong J (2008) Geometric Interpretation of Gene Co-Expression Network Analysis. PloS Computational Biology. 4(8): e1000117. PMID: 18704157 The WGCNA R package is described in • Langfelder P, Horvath S (2008) WGCNA: an R package for Weighted Correlation Network Analysis. BMC Bioinformatics. 2008 Dec 29;9(1):559. PMID: 19114008 For the generalized topological overlap matrix as applied to unweighted networks see • Yip A, Horvath S (2007) Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 8:22 2 Microarray Data The data and biological implications are described in Horvath et al 2006 The data were provided by Stan Nelson, who directs the UCLA Microarray core. Expression of 22,215 probe sets (15,005 unique transcripts) was measured using Affymetrix HG-U133A microarrays, as previously described(16). Data files (cel) were uploaded into the dCHIP program ( /) and normalized to the median intensity array. Complete gene expression for datasets 1 and 2 available at: /labs/horvath/binzhang/Public/Networks/GBM_all_datasets.zip; ( dataset 1 = gbm55old_dchipALL_cox.xls; dataset 2 = bm65new_dchipAll_cox.xls. Quantification was performed using model-based expression and the perfect match minus mismatch method implemented in dCHIP. More detailed descriptions and more detailed tutorials can be found at the following webpage: /labs/horvath/CoexpressionNetwork/ASPMgene/ Methods Outline The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. Here we study networks that can be specified with the following adjacency matrix: A=[aij] is symmetric with entries in [0,1]. By convention, the diagonal elements are assumed to be zero. For unweighted networks, the adjacency matrix contains binary information (connected=1, unconnected=0). In weighted networks the adjacency matrix contains weights. The absolute value of the Pearson correlation between expression profiles of all pairs of genes was determined for the 8000 most varying non-redundant transcripts. Then, pioneering the use of a novel approach to the generation of a weighted gene coexpression networks, the Pearson correlation measure was transformed into a connection strength measure by using a power function (connection strength(i,j)=|correlation(i,j)|^β)(Zhang and Horvath 2005). The connectivity measure for each gene is the sum of the connection strengths (correlationβ) between that gene and all the other genes in the network. Gene expression networks, like virtually all types of biological networks, exhibit an approximate scale free topology. A linear regression model fitting index R2 between log p(k) and log(k) was used to determine how well a resulting network fit scale free topology for a range of values. The scale free topplogy criterion (Zhang and Horvath 2005) was used to determine the power,: specifically, a value of =6 was the lowest power that resulted in a scale free topology fit R^2 that was >0.9 (Supplementary Fig. 1). We will discuss the choice of this power in great detail below and provide several arguments that this choice of power results in a biologically meaningful network. Most biologists would be very suspicious of a gene co-expression network that does not satisfy scale-free topology at least approximately. Therefore, thresholds (or powers) that give rise to networks that do not satisfy approximate scale-free topology should not be considered.