Spatial outliers represent locations which are significantly different from their neighborhoods
even though they may not be significantly different from the entire population. Identification …

Identification of outliers can lead to the discovery of unexpected, interesting, and useful
knowledge. Existing methods are designed for detecting spatial outliers in multidimensional …

Given a large spatio-temporal database of events, where each event consists of the fields
event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies …

Spatial data mining is the process of discovering interesting and previously unknown, but
potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns …

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide
pathological images (WSIs) can largely benefit therapy decision and prognosis …

Background The Warburg effect refers to glycolytic production of adenosine triphosphate
under aerobic conditions, and is a universal property of most cancer cells. Chronic …

Spatial Data Mining is the process of discovering interesting and previously unknown, but
potentially useful patterns from large spatial datasets. Extracting interesting and useful …

Identification of outliers can lead to the discovery of unexpected and interesting knowledge.
Existing methods are designed for detecting spatial outliers in multidimensional geometric …

A spatial time series dataset is a collection of time series, each referencing a location in a
common spatial framework. Correlation analysis is often used to identify pairs of potentially …

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently
located together in spatial proximity. Example co-location patterns include services …