Bussiness ManagementMBAstrategyHuman ResourceMarketingHospitalityE-commerceInternational Tradingproject managementmedia managementLogisticsFinanceAccountingadvertisingLawBusiness LawEducationEconomicsBusiness Reportbusiness planresearch proposal
英语论文题目英语教学英语论文商务英语英语论文格式商务英语翻译广告英语商务英语商务英语教学英语翻译论文英美文学英语语言学文化交流中西方文化差异英语论文范文英语论文开题报告初中英语教学英语论文文献综述英语论文参考文献
ResumeRecommendation LetterMotivation LetterPSapplication letterMBA essayBusiness Letteradmission letter Offer letter
澳大利亚论文英国论文加拿大论文芬兰论文瑞典论文澳洲论文新西兰论文法国论文香港论文挪威论文美国论文泰国论文马来西亚论文台湾论文新加坡论文荷兰论文南非论文西班牙论文爱尔兰论文
小学英语教学初中英语教学英语语法高中英语教学大学英语教学听力口语英语阅读英语词汇学英语素质教育英语教育毕业英语教学法
英语论文开题报告英语毕业论文写作指导英语论文写作笔记handbook英语论文提纲英语论文参考文献英语论文文献综述Research Proposal代写留学论文代写留学作业代写Essay论文英语摘要英语论文任务书英语论文格式专业名词turnitin抄袭检查
temcet听力雅思考试托福考试GMATGRE职称英语理工卫生职称英语综合职称英语职称英语
经贸英语论文题目旅游英语论文题目大学英语论文题目中学英语论文题目小学英语论文题目英语文学论文题目英语教学论文题目英语语言学论文题目委婉语论文题目商务英语论文题目最新英语论文题目英语翻译论文题目英语跨文化论文题目
日本文学日本语言学商务日语日本历史日本经济怎样写日语论文日语论文写作格式日语教学日本社会文化日语开题报告日语论文选题
职称英语理工完形填空历年试题模拟试题补全短文概括大意词汇指导阅读理解例题习题卫生职称英语词汇指导完形填空概括大意历年试题阅读理解补全短文模拟试题例题习题综合职称英语完形填空历年试题模拟试题例题习题词汇指导阅读理解补全短文概括大意
论文作者:www.51lunwen.org论文属性:学术文章 Scholarship Essay登出时间:2014-12-15编辑:Cinderella点击率:4668
论文字数:1287论文编号:org201411161752213483语种:英语 English地区:美国价格:免费论文
关键词:cluster analysis聚类分析法阶层式聚类分级法
摘要:本文介绍了一种重要的学术研究方法——聚类分析法。聚类分析是用于区分易管控、有价值的群组的重要的研究方法。学者用聚类分析来探究群体的相似性和不同。聚类分析的目标是使得相似的事物形成同类组织,因此所属于不同组织的事物就能被明显的区分开来。
This method is distinct from all other methods because it uses an analysis of variance approach to evaluate the distances between clusters. In short, this method attempts to minimize the Sum of Squares (SS) of any two (hypothetical) clusters that can be formed at each step. Typical of properties of variance for statistical decision-making, this tends to create two many clusters or clusters of small sizes because the more the observations scattered, the sum of squares makes the distance bigger.
Large datasets are possible with K-means clustering, unlike hierarchical clustering, because K-means clustering does not require prior computation of a proximity matrix of the distance/similarity of every case with every other case. Because cases may be shifted from one cluster to another during the iterative process of converging on a solution, k-means clustering is a type of 'relocation clustering method.' However, there is also a variant called 'agglomerative K-means clustering,' where the solution is constrained to force a given case to remain in its initial cluster.
Firstly K initial centroids are choosen, where K is a user-specified parameter, namely, the number of clusters desired. Every point is then assigned to the nearest centroid, and each collection of points assigned to a centroid is a cluster. The centroid of each cluster is then updated based on the points assigned to the cluster. We repeat the assignment and update steps until no point changes clusters, or equivalently, until the centroids remain the same.
K-means cluster analysis uses Euclidean distance. The researcher must specify in advance the desired number of clusters, K. Initial cluster centers are chosen randomly in a first pass of the data, then each additional iteration groups observations based on nearest Euclidean distance to the mean of the cluster. That is, the algorithm seeks to minimize within-cluster variance and maximize variability between clusters in an ANOVA-like fashion. Cluster centers change at each pass. The process continues until cluster means do not shift more than a given cut-off value or the iteration limit is reached.
4.2 Fuzzy Clustering
In a fuzzy clusterin, every objects belongs to each cluster with a membership rate that is between 0 (absolutely doesn’t belong) and 1 (absolutely belongs). In other words, clusters is viewed as fuzzy sets. (Mathematically, a fuzzy set is one in which an object belongs to any set with a weight that is between 0 and 1. In fuzzy clustering it pften impose the addtional constraint that the sum of the weights for each object must equal1.) similarly, pobablistic techniques compute the probability with which each point belongs to each cluster, and these probabilities must alsı sum to 1. Because the membership weights or probabilities for any object sum to 1, a fuzzy or probabilistic clustering doesn’t address true multiclass situations, such as the case of a student employee, where an object belongs to multiple classes本论文由英语论文网提供整理,提供论文代写,英语论文代写,代写论文,代写英语论文,代写留学生论文,代写英文论文,留学生论文代写相关核心关键词搜索。