Data mining :: practical machine learning tools and techniques /
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no al...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Boston, MA :
Morgan Kaufman,
2005.
|
| Ausgabe: | Second edition. |
| Schriftenreihe: | Morgan Kaufmann series in data management systems.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensiv inforation on neural networks; a new section on Bayesian networks; plus much more. Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you'll find: Algorithmic methods at the heart of successful data mining -- including tried and true techniques as well as leading edge methods; Performance improvement techniques that work by transforming the input or output; Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization -- in a new, interactive interface. -- |
| Beschreibung: | 1 online resource (xxxi, 525 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 485-503) and index. |
| ISBN: | 1423722442 9781423722441 008047702X 9780080477022 9780120884070 0120884070 9786611008062 6611008063 |
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| 245 | 1 | 0 | |a Data mining : |b practical machine learning tools and techniques / |c Ian H. Witten, Eibe Frank. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Amsterdam ; |a Boston, MA : |b Morgan Kaufman, |c 2005. | |
| 264 | 4 | |c ©2005 | |
| 300 | |a 1 online resource (xxxi, 525 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references (pages 485-503) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a pt. I. Machine learning tools and techniques. What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Transformations : engineering the input and output -- Moving on : extensions and applications -- pt. II. The Weka machine learning workbench. Introduction to Weka -- The Explorer -- The Knowledge Flow interface -- The Experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes. | |
| 520 | |a As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensiv inforation on neural networks; a new section on Bayesian networks; plus much more. Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you'll find: Algorithmic methods at the heart of successful data mining -- including tried and true techniques as well as leading edge methods; Performance improvement techniques that work by transforming the input or output; Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization -- in a new, interactive interface. -- |c Back cover. | ||
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 2 | |a Data Mining |0 https://id.nlm.nih.gov/mesh/D057225 | |
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| contents | pt. I. Machine learning tools and techniques. What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Transformations : engineering the input and output -- Moving on : extensions and applications -- pt. II. The Weka machine learning workbench. Introduction to Weka -- The Explorer -- The Knowledge Flow interface -- The Experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes. |
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| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Second edition. |
| format | Electronic eBook |
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I. Machine learning tools and techniques. What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Transformations : engineering the input and output -- Moving on : extensions and applications -- pt. II. The Weka machine learning workbench. Introduction to Weka -- The Explorer -- The Knowledge Flow interface -- The Experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensiv inforation on neural networks; a new section on Bayesian networks; plus much more. 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| genre | dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf |
| genre_facet | dissertations. Academic theses Academic theses. Thèses et écrits académiques. |
| id | ZDB-4-EBA-ocm61400355 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:15:46Z |
| institution | BVB |
| isbn | 1423722442 9781423722441 008047702X 9780080477022 9780120884070 0120884070 9786611008062 6611008063 |
| language | English |
| oclc_num | 61400355 |
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| physical | 1 online resource (xxxi, 525 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Morgan Kaufman, |
| record_format | marc |
| series | Morgan Kaufmann series in data management systems. |
| series2 | Morgan Kaufmann series in data management systems |
| spelling | Witten, I. H. (Ian H.) https://id.oclc.org/worldcat/entity/E39PBJgtcvTkCHwPWXvxbwgHYP http://id.loc.gov/authorities/names/n80102097 Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank. Second edition. Amsterdam ; Boston, MA : Morgan Kaufman, 2005. ©2005 1 online resource (xxxi, 525 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Morgan Kaufmann series in data management systems Includes bibliographical references (pages 485-503) and index. Print version record. pt. I. Machine learning tools and techniques. What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Transformations : engineering the input and output -- Moving on : extensions and applications -- pt. II. The Weka machine learning workbench. Introduction to Weka -- The Explorer -- The Knowledge Flow interface -- The Experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes. As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensiv inforation on neural networks; a new section on Bayesian networks; plus much more. Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you'll find: Algorithmic methods at the heart of successful data mining -- including tried and true techniques as well as leading edge methods; Performance improvement techniques that work by transforming the input or output; Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization -- in a new, interactive interface. -- Back cover. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Data mining fast Data mining. gtt Java (programmeertaal) gtt Machine-learning. gtt Algoritmen. gtt Descoberta de conhecimento. larpcal Mineração de dados. larpcal online searching. aat dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf Frank, Eibe. http://id.loc.gov/authorities/names/n99831139 has work: Data mining (Text) https://id.oclc.org/worldcat/entity/E39PCGP6KVKbcX4BhdymJk7Dmb https://id.oclc.org/worldcat/ontology/hasWork Print version: Witten, I.H. (Ian H.). Data mining. 2nd ed. Amsterdam ; Boston, MA : Morgan Kaufman, 2005 0120884070 (DLC) 2005043385 (OCoLC)58451668 Morgan Kaufmann series in data management systems. http://id.loc.gov/authorities/names/n90627178 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=130260 Volltext |
| spellingShingle | Witten, I. H. (Ian H.) Data mining : practical machine learning tools and techniques / Morgan Kaufmann series in data management systems. pt. I. Machine learning tools and techniques. What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Transformations : engineering the input and output -- Moving on : extensions and applications -- pt. II. The Weka machine learning workbench. Introduction to Weka -- The Explorer -- The Knowledge Flow interface -- The Experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Data mining fast Data mining. gtt Java (programmeertaal) gtt Machine-learning. gtt Algoritmen. gtt Descoberta de conhecimento. larpcal Mineração de dados. larpcal online searching. aat |
| subject_GND | http://id.loc.gov/authorities/subjects/sh97002073 https://id.nlm.nih.gov/mesh/D057225 http://id.loc.gov/authorities/genreForms/gf2014026039 |
| title | Data mining : practical machine learning tools and techniques / |
| title_auth | Data mining : practical machine learning tools and techniques / |
| title_exact_search | Data mining : practical machine learning tools and techniques / |
| title_full | Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank. |
| title_fullStr | Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank. |
| title_full_unstemmed | Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank. |
| title_short | Data mining : |
| title_sort | data mining practical machine learning tools and techniques |
| title_sub | practical machine learning tools and techniques / |
| topic | Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Data mining fast Data mining. gtt Java (programmeertaal) gtt Machine-learning. gtt Algoritmen. gtt Descoberta de conhecimento. larpcal Mineração de dados. larpcal online searching. aat |
| topic_facet | Data mining. Data Mining Exploration de données (Informatique) COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Data mining Java (programmeertaal) Machine-learning. Algoritmen. Descoberta de conhecimento. Mineração de dados. online searching. dissertations. Academic theses Academic theses. Thèses et écrits académiques. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=130260 |
| work_keys_str_mv | AT wittenih dataminingpracticalmachinelearningtoolsandtechniques AT frankeibe dataminingpracticalmachinelearningtoolsandtechniques |