پديد آورنده :
هاتفي، الهام
عنوان :
كاوش قوانين وابستگي فازي با حفظ حريم شخصي
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
كامپيوتر - هوش مصنوعي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر
صفحه شمار :
هجده، 89ص.: مصور، جدول، نمودار
استاد راهنما :
عبدالرضا ميرزايي
استاد مشاور :
مهران صفاياني
توصيفگر ها :
داده كاوي , پنهان سازي قوانين وابستگي , بهينه سازي محدب
تاريخ نمايه سازي :
1394/05/28
تاريخ ورود اطلاعات :
1396/10/04
رشته تحصيلي :
برق و كامپيوتر
دانشكده :
مهندسي برق و كامپيوتر
چكيده فارسي :
به فارسي و انگليسي
چكيده انگليسي :
Privacy Preserving Fuzzy Association Rule Mining Elham Hatefi e hatefi@ec iut ac ir Date Of Submission 2015 06 17 Department of Electrical And Computer Engineering Isfahan University Of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiSupervisor Abdolreza Mirzaei mirzaei@cc iut ac irAbstract Privacy preserving data mining PPDM has been a new research area in the past two decades Infact the aim of PPDM algorithms is to modify data in the dataset so that sensitive data andconfidential knowledge even after data mining operation be kept confidential Association rulehiding is one of the main techniques of PPDM aiming to avoid extracting some rules that arerecognized as sensitive rules Most of the work which has been done in the area of association rulehiding are limited to binary data however many real world datasets include quantitative data too In this work a new methods is proposed to hide sensitive quantitative association rules which isbased on convex optimization technique In most of the previous methods there were uniformchanges in the values of all items and also in all of them the correlation between related items werenot considered By considering these two issues fewer changes are made in the real and fuzzydataset In our proposed method we make suitable changes in the value of each item and therelations between related items are defined as constraints in the optimization problem In allexisting methods at this field association rules were extracted from 2 large itemsets However ourmethod can be extended for any kind of association rules The performance of the proposedalgorithm is measured in the term of percentage of hiding of sensitive rules side effects andchanges occurred in the fuzzy and real datasets The results showed most sensitive rules weremade hidden with the proposed method and the number of lost and ghost rules and changes in thefuzzy and real datasets have been significantly reduced Keyword Data Mining Association Rule Hiding Convex Optimization
استاد راهنما :
عبدالرضا ميرزايي
استاد مشاور :
مهران صفاياني
