Knowledge Discovery and Measures of Interest:
Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity mea...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2001
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
638 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity measures are used to rank summaries generated from databases. The knowledge discovery practitioner will find solid empirical evidence on which to base decisions regarding the choice of measures in data mining applications. The knowledge discovery student in a senior undergraduate or graduate course in databases and data mining will find the book is a good introduction to the concepts and techniques of measures of interest. In Knowledge Discovery and Measures of Interest, we study two closely related steps in any knowledge discovery system: the generation of discovered knowledge; and the interpretation and evaluation of discovered knowledge. In the generation step, we study data summarization, where a single dataset can be generalized in many different ways and to many different levels of granularity according to domain generalization graphs. In the interpretation and evaluation step, we study diversity measures as heuristics for ranking the interestingness of the summaries generated. The objective of this work is to introduce and evaluate a technique for ranking the interestingness of discovered patterns in data. It consists of four primary goals: To introduce domain generalization graphs for describing and guiding the generation of summaries from databases. To introduce and evaluate serial and parallel algorithms that traverse the domain generalization space described by the domain generalization graphs. To introduce and evaluate diversity measures as heuristic measures of interestingness for ranking summaries generated from databases. To develop the preliminary foundation for a theory of interestingness within the context of ranking summaries generated from databases. Knowledge Discovery and Measures of Interest is suitable as a secondary text in a graduate level course and as a reference for researchers and practitioners in industry |
| Beschreibung: | 1 Online-Ressource (XVIII, 162 p) |
| ISBN: | 9781475732832 |
| DOI: | 10.1007/978-1-4757-3283-2 |
Internformat
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| 520 | |a Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity measures are used to rank summaries generated from databases. The knowledge discovery practitioner will find solid empirical evidence on which to base decisions regarding the choice of measures in data mining applications. The knowledge discovery student in a senior undergraduate or graduate course in databases and data mining will find the book is a good introduction to the concepts and techniques of measures of interest. In Knowledge Discovery and Measures of Interest, we study two closely related steps in any knowledge discovery system: the generation of discovered knowledge; and the interpretation and evaluation of discovered knowledge. | ||
| 520 | |a In the generation step, we study data summarization, where a single dataset can be generalized in many different ways and to many different levels of granularity according to domain generalization graphs. In the interpretation and evaluation step, we study diversity measures as heuristics for ranking the interestingness of the summaries generated. The objective of this work is to introduce and evaluate a technique for ranking the interestingness of discovered patterns in data. It consists of four primary goals: To introduce domain generalization graphs for describing and guiding the generation of summaries from databases. To introduce and evaluate serial and parallel algorithms that traverse the domain generalization space described by the domain generalization graphs. To introduce and evaluate diversity measures as heuristic measures of interestingness for ranking summaries generated from databases. | ||
| 520 | |a To develop the preliminary foundation for a theory of interestingness within the context of ranking summaries generated from databases. Knowledge Discovery and Measures of Interest is suitable as a secondary text in a graduate level course and as a reference for researchers and practitioners in industry | ||
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Datensatz im Suchindex
| _version_ | 1804178819948478464 |
|---|---|
| any_adam_object | |
| author | Hilderman, Robert J. Hamilton, Howard J. |
| author_facet | Hilderman, Robert J. Hamilton, Howard J. |
| author_role | aut aut |
| author_sort | Hilderman, Robert J. |
| author_variant | r j h rj rjh h j h hj hjh |
| building | Verbundindex |
| bvnumber | BV045149004 |
| collection | ZDB-2-ENG |
| ctrlnum | (ZDB-2-ENG)978-1-4757-3283-2 (OCoLC)1184499014 (DE-599)BVBBV045149004 |
| dewey-full | 005.74 |
| dewey-hundreds | 000 - Computer science, information, general works |
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| dewey-raw | 005.74 |
| dewey-search | 005.74 |
| dewey-sort | 15.74 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4757-3283-2 |
| format | Electronic eBook |
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| id | DE-604.BV045149004 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:02Z |
| institution | BVB |
| isbn | 9781475732832 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030538703 |
| oclc_num | 1184499014 |
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| owner_facet | DE-573 DE-634 |
| physical | 1 Online-Ressource (XVIII, 162 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_Archiv |
| publishDate | 2001 |
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| publisher | Springer US |
| record_format | marc |
| series2 | The Springer International Series in Engineering and Computer Science |
| spelling | Hilderman, Robert J. Verfasser aut Knowledge Discovery and Measures of Interest by Robert J. Hilderman, Howard J. Hamilton Boston, MA Springer US 2001 1 Online-Ressource (XVIII, 162 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 638 Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity measures are used to rank summaries generated from databases. The knowledge discovery practitioner will find solid empirical evidence on which to base decisions regarding the choice of measures in data mining applications. The knowledge discovery student in a senior undergraduate or graduate course in databases and data mining will find the book is a good introduction to the concepts and techniques of measures of interest. In Knowledge Discovery and Measures of Interest, we study two closely related steps in any knowledge discovery system: the generation of discovered knowledge; and the interpretation and evaluation of discovered knowledge. In the generation step, we study data summarization, where a single dataset can be generalized in many different ways and to many different levels of granularity according to domain generalization graphs. In the interpretation and evaluation step, we study diversity measures as heuristics for ranking the interestingness of the summaries generated. The objective of this work is to introduce and evaluate a technique for ranking the interestingness of discovered patterns in data. It consists of four primary goals: To introduce domain generalization graphs for describing and guiding the generation of summaries from databases. To introduce and evaluate serial and parallel algorithms that traverse the domain generalization space described by the domain generalization graphs. To introduce and evaluate diversity measures as heuristic measures of interestingness for ranking summaries generated from databases. To develop the preliminary foundation for a theory of interestingness within the context of ranking summaries generated from databases. Knowledge Discovery and Measures of Interest is suitable as a secondary text in a graduate level course and as a reference for researchers and practitioners in industry Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Discrete Mathematics in Computer Science Theory of Computation Computer science Data structures (Computer science) Computers Computer science / Mathematics Artificial intelligence Datenbank (DE-588)4011119-2 gnd rswk-swf Expertensystem (DE-588)4113491-6 gnd rswk-swf Datenbank (DE-588)4011119-2 s Expertensystem (DE-588)4113491-6 s 1\p DE-604 Hamilton, Howard J. aut Erscheint auch als Druck-Ausgabe 9781441949134 https://doi.org/10.1007/978-1-4757-3283-2 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hilderman, Robert J. Hamilton, Howard J. Knowledge Discovery and Measures of Interest Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Discrete Mathematics in Computer Science Theory of Computation Computer science Data structures (Computer science) Computers Computer science / Mathematics Artificial intelligence Datenbank (DE-588)4011119-2 gnd Expertensystem (DE-588)4113491-6 gnd |
| subject_GND | (DE-588)4011119-2 (DE-588)4113491-6 |
| title | Knowledge Discovery and Measures of Interest |
| title_auth | Knowledge Discovery and Measures of Interest |
| title_exact_search | Knowledge Discovery and Measures of Interest |
| title_full | Knowledge Discovery and Measures of Interest by Robert J. Hilderman, Howard J. Hamilton |
| title_fullStr | Knowledge Discovery and Measures of Interest by Robert J. Hilderman, Howard J. Hamilton |
| title_full_unstemmed | Knowledge Discovery and Measures of Interest by Robert J. Hilderman, Howard J. Hamilton |
| title_short | Knowledge Discovery and Measures of Interest |
| title_sort | knowledge discovery and measures of interest |
| topic | Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Discrete Mathematics in Computer Science Theory of Computation Computer science Data structures (Computer science) Computers Computer science / Mathematics Artificial intelligence Datenbank (DE-588)4011119-2 gnd Expertensystem (DE-588)4113491-6 gnd |
| topic_facet | Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Discrete Mathematics in Computer Science Theory of Computation Computer science Data structures (Computer science) Computers Computer science / Mathematics Artificial intelligence Datenbank Expertensystem |
| url | https://doi.org/10.1007/978-1-4757-3283-2 |
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