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ocn182727695 |
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OCoLC |
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20240504213016.0 |
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m o d |
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cr cnu---unuuu |
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071203s2006 si a ob 001 0 eng d |
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|a 9812773630
|q (electronic bk.)
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|a QA76.9.D343
|b L43 2006eb
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|x 084010
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|a HCDD
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|a Lecture notes in data mining /
|c edited by Michael W. Berry, Murray Browne.
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|a Singapore ;
|a Hackensack, NJ :
|b World Scientific,
|c ©2006.
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| 300 |
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|a 1 online resource (xiii, 222 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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|a Includes bibliographical references and index.
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|a Print version record.
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|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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|a 1. Point estimation algorithms -- 2. Applications of Bayes Theorem -- 3. Similarity measures -- 4. Decision trees -- 5. Genetic algorithms -- 6. Classification: distance-based algorithms -- 7. Decision tree-based algorithms -- 8. Covering (rule-based) algorithms -- 9. Clustering: an overview -- 10. Clustering: hierarchical algorithms -- ch. 11. Clustering: partitional algorithms -- 12. Clustering: large databases -- 13. Clustering: categorical attributes -- 14. Association rules: an overview -- 15. Association rules: parallel and distributed algorithms -- 16. Association rules: advanced techniques and measures -- 17. Spatial mining: techniques and algorithms.
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| 650 |
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|a Data mining.
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| 650 |
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7 |
|a COMPUTERS
|x Desktop Applications
|x Databases.
|2 bisacsh
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| 650 |
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|a COMPUTERS
|x Database Management
|x General.
|2 bisacsh
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| 650 |
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|a COMPUTERS
|x System Administration
|x Storage & Retrieval.
|2 bisacsh
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| 650 |
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|a Data mining
|2 fast
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| 650 |
1 |
7 |
|a Datamining.
|2 gtt
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| 700 |
1 |
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|a Berry, Michael W.
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| 700 |
1 |
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|a Browne, Murray.
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| 758 |
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|i has work:
|a Lecture notes in data mining (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGWKkTddfWkCh7D6TwTXbb
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|t Lecture notes in data mining.
|d Singapore ; Hackensack, NJ : World Scientific, ©2006
|z 9812568026
|z 9789812568021
|w (DLC) 2007271205
|w (OCoLC)81150806
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=1681620
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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