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Preference-based Spatial Co-location Pattern Mining (Hardcover, 1st ed. 2022)
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Preference-based Spatial Co-location Pattern Mining (Hardcover, 1st ed. 2022)
Series: Big Data Management
Expected to ship within 9 - 17 working days
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The development of information technology has made it possible to
collect large amounts of spatial data on a daily basis. It is of
enormous significance when it comes to discovering implicit,
non-trivial and potentially valuable information from this spatial
data. Spatial co-location patterns reveal the distribution rules of
spatial features, which can be valuable for application users. This
book provides commercial software developers with proven and
effective algorithms for detecting and filtering these implicit
patterns, and includes easily implemented pseudocode for all the
algorithms. Furthermore, it offers a basis for further research in
this promising field. Preference-based co-location pattern mining
refers to mining constrained or condensed co-location patterns
instead of mining all prevalent co-location patterns. Based on the
authors' recent research, the book highlights techniques for
solving a range of problems in this context, including maximal
co-location pattern mining, closed co-location pattern mining,
top-k co-location pattern mining, non-redundant co-location pattern
mining, dominant co-location pattern mining, high utility
co-location pattern mining, user-preferred co-location pattern
mining, and similarity measures between spatial co-location
patterns. Presenting a systematic, mathematical study of
preference-based spatial co-location pattern mining, this book can
be used both as a textbook for those new to the topic and as a
reference resource for experienced professionals.
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