Lecture notes in data mining:
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
c2006
|
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references and index The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering, and association rules, this book also considers alternative candidates such as point estimation and genetic algorithms. The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining |
| Beschreibung: | 1 Online-Ressource (xiii, 222 p.) |
| ISBN: | 1281379042 9781281379047 9789812773630 9812773630 |
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| 500 | |a Includes bibliographical references and index | ||
| 500 | |a The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering, and association rules, this book also considers alternative candidates such as point estimation and genetic algorithms. The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining | ||
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| isbn | 1281379042 9781281379047 9789812773630 9812773630 |
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| spelling | Lecture notes in data mining edited by Michael W. Berry, Murray Browne Singapore World Scientific c2006 1 Online-Ressource (xiii, 222 p.) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering, and association rules, this book also considers alternative candidates such as point estimation and genetic algorithms. The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining COMPUTERS / Desktop Applications / Databases bisacsh COMPUTERS / Database Management / General bisacsh COMPUTERS / System Administration / Storage & Retrieval bisacsh Datamining gtt Data mining fast Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s 1\p DE-604 Berry, Michael W. Sonstige oth Browne, Murray Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=210789 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Lecture notes in data mining COMPUTERS / Desktop Applications / Databases bisacsh COMPUTERS / Database Management / General bisacsh COMPUTERS / System Administration / Storage & Retrieval bisacsh Datamining gtt Data mining fast Data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Lecture notes in data mining |
| title_auth | Lecture notes in data mining |
| title_exact_search | Lecture notes in data mining |
| title_full | Lecture notes in data mining edited by Michael W. Berry, Murray Browne |
| title_fullStr | Lecture notes in data mining edited by Michael W. Berry, Murray Browne |
| title_full_unstemmed | Lecture notes in data mining edited by Michael W. Berry, Murray Browne |
| title_short | Lecture notes in data mining |
| title_sort | lecture notes in data mining |
| topic | COMPUTERS / Desktop Applications / Databases bisacsh COMPUTERS / Database Management / General bisacsh COMPUTERS / System Administration / Storage & Retrieval bisacsh Datamining gtt Data mining fast Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | COMPUTERS / Desktop Applications / Databases COMPUTERS / Database Management / General COMPUTERS / System Administration / Storage & Retrieval Datamining Data mining Data Mining |
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