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[null,"1.","Record Nr.",null,"UNINA9910743371903321"]
[null,null,"Autore",null,"Wang Lizhen"]
[null,null,"Titolo",null,"Preference-based Spatial Co-location Pattern Mining / / by Lizhen Wang, Yuan Fang, Lihua Zhou"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"9789811675669"]
[{"$nulls":4},"981167566X"]
[{"$nulls":4},"9789811675652"]
[{"$nulls":4},"9811675651"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (307 pages)"]
[null,null,"Collana",null,"Big Data Management, , 2522-0187"]
[null,null,"Disciplina",null,"005.7"]
[null,null,"Soggetti",null,"Computer science"]
[{"$nulls":4},"Blockchains (Databases)"]
[{"$nulls":4},"Data protection"]
[{"$nulls":4},"Computer Science"]
[{"$nulls":4},"Blockchain"]
[{"$nulls":4},"Data and Information Security"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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."]