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220201 ||| eng |
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|a 9789811675669
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| 100 |
1 |
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|a Wang, Lizhen
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| 245 |
0 |
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|a Preference-based Spatial Co-location Pattern Mining
|h Elektronische Ressource
|c by Lizhen Wang, Yuan Fang, Lihua Zhou
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| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2022, 2022
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| 300 |
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|a XVI, 294 p. 1 illus
|b online resource
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| 505 |
0 |
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|a Chapter 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
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| 653 |
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|a Computer science
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| 653 |
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|a Computer Science
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| 653 |
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|a Blockchains (Databases)
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| 653 |
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|a Data protection
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| 653 |
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|a Blockchain
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| 653 |
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|a Data and Information Security
|
| 700 |
1 |
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|a Fang, Yuan
|e [author]
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| 700 |
1 |
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|a Zhou, Lihua
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Big Data Management
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| 028 |
5 |
0 |
|a 10.1007/978-981-16-7566-9
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-16-7566-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 004
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| 520 |
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|a 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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