LEADER 04247nam 22006615 450 001 9910743371903321 005 20251113210125.0 010 $a9789811675669 010 $a981167566X 010 $a9789811675652 010 $a9811675651 024 7 $a10.1007/978-981-16-7566-9 035 $a(MiAaPQ)EBC6841148 035 $a(Au-PeEL)EBL6841148 035 $a(CKB)20462370900041 035 $a(OCoLC)1291317151 035 $a(PPN)262174855 035 $a(DE-He213)978-981-16-7566-9 035 $a(EXLCZ)9920462370900041 100 $a20220104d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPreference-based Spatial Co-location Pattern Mining /$fby Lizhen Wang, Yuan Fang, Lihua Zhou 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (307 pages) 225 1 $aBig Data Management,$x2522-0187 311 08$aPrint version: Wang, Lizhen Preference-Based Spatial Co-location Pattern Mining Singapore : Springer Singapore Pte. Limited,c2022 9789811675652 311 08$a9811675651 327 $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. 330 $aThe development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field. Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors? recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns. Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals. 410 0$aBig Data Management,$x2522-0187 606 $aComputer science 606 $aBlockchains (Databases) 606 $aData protection 606 $aComputer Science 606 $aBlockchain 606 $aData and Information Security 615 0$aComputer science. 615 0$aBlockchains (Databases) 615 0$aData protection. 615 14$aComputer Science. 615 24$aBlockchain. 615 24$aData and Information Security. 676 $a005.7 700 $aWang$b Lizhen$01426676 702 $aFang$b Yuan 702 $aZhou$b Lihua 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910743371903321 996 $aPreference-based spatial co-location pattern mining$93558731 997 $aUNINA