Definability in Mining Incomplete Data

Vertical Mining with Incomplete Data

Mining frequent patterns is essential in many data mining methods. Frequent patterns lead to the discovery of association rules, strong rules, sequential episodes, and multi-dimensional patterns. Patterns should be discovered in a time and space efficient manner. Vertical mining algorithms key advantage is that they can outperform their horizontal counterparts in terms of both time and space ef...

the clustering and classification data mining techniques in insurance fraud detection:the case of iranian car insurance

با توجه به گسترش روز افزون تقلب در حوزه بیمه به خصوص در بخش بیمه اتومبیل و تبعات منفی آن برای شرکت های بیمه، به کارگیری روش های مناسب و کارآمد به منظور شناسایی و کشف تقلب در این حوزه امری ضروری است. درک الگوی موجود در داده های مربوط به مطالبات گزارش شده گذشته می تواند در کشف واقعی یا غیرواقعی بودن ادعای خسارت، مفید باشد. یکی از متداول ترین و پرکاربردترین راه های کشف الگوی داده ها استفاده از ر...

Image Reconstruction from Incomplete Data

data mining rules and classification methods in insurance: the case of collision insurance

assigning premium to the insurance contract in iran mostly has based on some old rules have been authorized by government, in such a situation predicting premium by analyzing database and it’s characteristics will be definitely such a big mistake. therefore the most beneficial information one can gathered from these data is the amount of loss happens during one contract to predicting insurance ...

Mining Incomplete Data - A Rough Set Approach