This paper presents a new decision tree induction method, called co-location-based
decision tree (CL-DT), to enhance the decision-making of pavement maintenance and …

Recent developments in data mining and machine learning approaches have brought lots of
excitement in providing solutions for challenging tasks (eg, computer vision). However …

The proliferation in amounts of generated data has propelled the rise of scalable machine
learning solutions to efficiently analyze and extract useful insights from such data …

The detection of colocation pattern is an important and widely used method to analyze the
spatial associations of geographical objects and events. Existing studies primarily focus on …

In many application domains, occurrences of related spatial features may exhibit co-location
pattern. For example, some disease may be in spatial proximity of certain type of pollution …

Co-location pattern mining detects sets of features frequently located in close proximity to
each other. This book focuses on data mining for co-location pattern, a valid method for …

Co‐location patterns are the subsets of Boolean spatial features whose instances are often
located in close geographic proximity. Neighborhood is a major challenge and a key part of …

Co-location mining is a classical problem in spatial pattern mining. Considering a set of
boolean spatial features, the goal is to find subsets of features frequently located together. It …

Existing co-location mining algorithms require a user provided distance threshold at which
prevalent patterns are searched. Since spatial interactions, in reality, may happen at …

Spatial co-location patterns represent the subsets of features (co-location) whose events are
frequently located together in geographic space. Spatio-temporal co-location (co …