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020 _a9789811675669
_9978-981-16-7566-9
024 7 _a10.1007/978-981-16-7566-9
_2doi
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aCOM000000
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072 7 _aUY
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082 0 4 _a004
_223
100 1 _aWang, Lizhen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aPreference-based Spatial Co-location Pattern Mining
_h[electronic resource] /
_cby Lizhen Wang, Yuan Fang, Lihua Zhou.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXVI, 294 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aBig Data Management,
_x2522-0187
505 0 _aChapter 1: Introduction -- Chapter 2: Maximal Prevalent Co-location Patterns -- Chapter 3: Maximal Sub-prevalent Co-location Patterns -- Chapter 4: SPI-Closed Prevalent Co-location Patterns -- Chapter 5: Top-k Probabilistically Prevalent Co-location Patterns -- Chapter 6: Non-Redundant Prevalent Co-location Patterns -- Chapter 7: Dominant Spatial Co-location Patterns -- Chapter 8: High Utility Co-location Patterns -- Chapter 9: High Utility Co-location Patterns with Instance Utility -- Chapter 10: Interactively Post-mining User-preferred Co-location Pat-terns with a Probabilistic Model -- Chapter 11: Vector-Degree: A General Similarity Measure for Spatial Co-Location Patterns.
520 _aThe 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.
650 0 _aComputer science.
650 0 _aBlockchains (Databases).
650 0 _aData protection.
650 1 4 _aComputer Science.
650 2 4 _aBlockchain.
650 2 4 _aData and Information Security.
700 1 _aFang, Yuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhou, Lihua.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811675652
776 0 8 _iPrinted edition:
_z9789811675676
776 0 8 _iPrinted edition:
_z9789811675683
830 0 _aBig Data Management,
_x2522-0187
856 4 0 _uhttps://doi.org/10.1007/978-981-16-7566-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c179180
_d179180