An Application of Association Rule Mining in Animal Sciences: Mining the relationship between Chemical Composition of meat and Breeding levels
محل انتشار: اولین کنفرانس داده کاوی ایران
سال انتشار: 1386
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 2,454
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شناسه ملی سند علمی:
IDMC01_091
تاریخ نمایه سازی: 20 خرداد 1386
چکیده مقاله:
The need to reduce the fat content of the carcasses of meat-producing species have motivated research for finding ways of producing carcasses with lower levels of fat. Determining chemical carcass/body composition of animals and its relationship with breeding programs and levels of
feeding are major problems considered by researchers in this field. In this article Association Rule Mining technique is applied to identify patterns of interest in our dataset (i.e., sheep dataset). Association rules can reveal relevant associations between different breed or feeding levels of sheep and fat amount of meet or other chemical composition of meat in most carcass cuts and carcass meat. Since our sheep dataset contains quantitative attributes, we cannot directly apply binary algorithms. Therefore, we either have to transform the quantitative problem into binary one or to use Fuzzy Association rule mining. We have used fuzzy association rules mining to deal with both continuous (numerical) and discrete (nominal) attributes in our dataset. We found numerous useful rules in the data. A cursory analysis of some of these rules reveals numerous associations between amount of certain chemicals in different part of carcasses, many of which make sense to animal scientists, others suggesting new hypotheses that may warrant further investigation.
کلیدواژه ها:
نویسندگان
Ashoor Mahani
Department of Computer Science & Engineering, Shiraz University
Sadreddini
Department of Computer Science & Engineering, Shiraz University
Zamiri
Department of Animal Science, College of Agriculture, Shiraz University