Spatial clustering and its effect on perceived clustering, numerosity, and dispersion
نویسندگان
چکیده
منابع مشابه
Spatial clustering and its effect on perceived clustering, numerosity, and dispersion
Human observers are able to estimate the numerosity of large sets of visual elements. The occupancy model of perceived numerosity in intermediate numerical ranges is based on overlapping regions of influence. The key idea is that items within a certain range count for less than their actual numerical value and more so the closer they are to their neighbours. Therefore occupancy is sensitive to ...
متن کاملWhen More Seems Less – Non-Spatial Clustering in Numerosity Estimation
How is numerosity estimation affected by additional structural information in visual displays? Two experiments investigated if the linking of dots by line segments, thereby forming clusters of polygons, leads to an underestimation effect similar to that observed in classical experiments on clustering by spatial proximity. Our findings confirmed such an underestimation effect for non-spatial clu...
متن کاملSpatial data compression via adaptive dispersion clustering
In this article, we introduce a method of spatial data compression, which we call Adaptive Spatial Dispersion Clustering (ASDC). It is specifically designed to reduce the size of a spatial dataset in order to facilitate subsequent spatial prediction. Unlike with traditional data and image compression methods, the goal of ASDC is to create a new dataset that will be used as input into spatial pr...
متن کاملA Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...
متن کاملthe 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...
ذخیره 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: Attention, Perception, & Psychophysics
سال: 2016
ISSN: 1943-3921,1943-393X
DOI: 10.3758/s13414-016-1100-0