پديد آورنده :
گوانجي، رويا
عنوان :
خوشه بندي مدل-پايه ي داده هاي بيان ژن با استفاده از توزيع آميخته ي چند متغيره ي t
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
آمار اقتصادي و اجتماعي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي
صفحه شمار :
چهارده،103ص.: مصور،جدول،نمودار(رنگي)
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
سروش عليمرادي
استاد مشاور :
علي زينل همداني
توصيفگر ها :
مدل آميخته متناهي , تجزيه چالسكي
تاريخ نمايه سازي :
10/4/93
استاد داور :
ايرج كاظمي، صفيه محمودي
چكيده انگليسي :
Model based clustering of gene expression data using mixture of multivariate t distribution Roya Gavanji r gavanji@math iut ac ir 22 january 2014 Department of Mathematical Sciences Isfahan University of Technology Isfahan 84156 83111 Iran Supervisor Dr Soroush Alimoradi salimora@cc iut ac ir Advisor Dr Ali Zeynal Hamadani hamadani@cc iut ac ir 2013 MSC 62H30 Keywords Clustering Gene expression Finite mixture model Cholesky decomposition Multi variate t distribution AbstractNowadays most of what we consider information is stored on computers and the amount of data beingcollected is increasing This is where the ability of humans to distinguish groups degrades Therefore we need some methods and techniques to summarize and extract these data One of these techniquesis Clustering The term Clustering refers to the grouping of data without any a priori knowledge ofwhat groups are present in the data Clustering is the task of grouping a set of objects in such a waythat objects in the same group called a cluster are more similar in some sense or another to eachother than to those in other groups clusters Clustering is a main task of exploring data mining and a common technique for statistical data analysis used in many elds including machine learning pattern recognition image analysis information retrieval and bioinformatics Recently longitudinaldata which collected over period of time from speci c units has been more attentive In this thesis longitudinal data are clustered using Gaussian and Non Gaussian mixture distributions with consideration of the appropriate covariance structure of these data Mixture model based cluster ing has been become an increasingly popular data analysis technique since fty years ago Families ofmixture models are said to arise when the component parameters usually the component covariancematrices are decomposed and a number of constraints are imposed Within the family setting it is
استاد راهنما :
سروش عليمرادي
استاد مشاور :
علي زينل همداني
استاد داور :
ايرج كاظمي، صفيه محمودي
