Combination Rules of Spatial Geoscience Data for Mineral Exploration
نویسندگان
چکیده
منابع مشابه
Interactive Data Exploration for Geoscience
Data-driven research requires interactive systems supporting fast and intuitive data exploration. An important component is the user interface that facilitates this process. In biodiversity research, data is commonly of spatio-temporal nature. This poses unique opportunities for visual analytics approaches. In this paper we present the core concepts of the web-based front end of our VAT (Visual...
متن کاملAllocentric coding: Spatial range and combination rules
When a visual target is presented with neighboring landmarks, its location can be determined both relative to the self (egocentric coding) and relative to these landmarks (allocentric coding). In the present study, we investigated (1) how allocentric coding depends on the distance between the targets and their surrounding landmarks (i.e. the spatial range) and (2) how allocentric and egocentric...
متن کاملMineral Exploration Using Modern Data Mining Techniques
Returns from gold exploration have been disappointing over the last 20 years, despite the surge in quality and quantity of exploration data. Historically, major discoveries have occurred in waves following the introduction of new methods. This paper argues that the new methods driving the next wave of discoveries will be found in recent developments in data mining techniques, including visualiz...
متن کاملdata 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 ...
15 صفحه اولOn Mining Fuzzy Classification Rules for Imbalanced Data
Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Geoinformatics
سال: 1991
ISSN: 0388-502X,1347-541X
DOI: 10.6010/geoinformatics1990.2.2_159