Predictive analytics and data mining : concepts and practice with RapidMiner /
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| 格式: | 图书 |
| 语言: | 英语 |
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Amsterdam :
Elsevier/Morgan Kaufmann, Morgan Kaufmann is an imprint of Elsevier,
[2015]
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| 在线阅读: | E-book - Full text from ebookcentral |
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| 245 | 1 | 0 | |a Predictive analytics and data mining : |b concepts and practice with RapidMiner / |c Vijay Kotu, Bala Deshpande, PhD. |
| 264 | 1 | |a Amsterdam : |b Elsevier/Morgan Kaufmann, Morgan Kaufmann is an imprint of Elsevier, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a xix, 425 pages : |b illustrations ; |c 24 cm | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g 1.1. |t What Data Mining Is -- |g 1.2. |t What Data Mining Is Not -- |g 1.3. |t Case for Data Mining -- |g 1.4. |t Types of Data Mining -- |g 1.5. |t Data Mining Algorithms -- |g 1.6. |t Roadmap for Upcoming Chapters -- |g 2.1. |t Prior Knowledge -- |g 2.2. |t Data Preparation -- |g 2.3. |t Modeling -- |g 2.4. |t Application -- |g 2.5. |t Knowledge -- |g 3.1. |t Objectives of Data Exploration -- |g 3.2. |t Data Sets -- |g 3.3. |t Descriptive Statistics -- |g 3.4. |t Data Visualization -- |g 3.5. |t Roadmap for Data Exploration -- |g 4.1. |t Decision Trees -- |g 4.2. |t Rule Induction -- |g 4.3. |t k-Nearest Neighbors -- |g 4.4. |t Naïve Bayesian -- |g 4.5. |t Artificial Neural Networks -- |g 4.6. |t Support Vector Machines -- |g 4.7. |t Ensemble Learners -- |g 5.1. |t Linear Regression -- |g 5.2. |t Logistic Regression -- |g 6.1. |t Concepts of Mining Association Rules -- |g 6.2. |t Apriori Algorithm -- |g 6.3. |t FP-Growth Algorithm -- |g 7.1. |t Types of Clustering Techniques -- |g 7.2. |t k-Means Clustering -- |g 7.3. |t DBSCAN Clustering -- |g 7.4. |t Self-Organizing Maps -- |g 8.1. |t Confusion Matrix (or Truth Table) -- |g 8.2. |t Receiver Operator Characteristic (ROC) Curves and Area under the Curve (AUC) -- |g 8.3. |t Lift Curves -- |g 8.4. |t Evaluating the Predictions: Implementation -- |g 9.1. |t How Text Mining Works -- |g 9.2. |t Implementing Text Mining with Clustering and Classification -- |g 10.1. |t Data-Driven Approaches -- |g 10.2. |t Model-Driven Forecasting Methods -- |g 11.1. |t Anomaly Detection Concepts -- |g 11.2. |t Distance-Based Outlier Detection -- |g 11.3. |t Density-Based Outlier Detection -- |g 11.4. |t Local Outlier Factor -- |g 12.1. |t Classifying Feature Selection Methods -- |g 12.2. |t Principal Component Analysis -- |g 12.3. |t Information Theory-Based Filtering for Numeric Data -- |g 12.4. |t Chi-Square-Based Filtering for Categorical Data -- |g 12.5. |t Wrapper-Type Feature Selection -- |g 13.1. |t User Interface and Terminology -- |g 13.2. |t Data Importing and Exporting Tools -- |g 13.3. |t Data Visualization Tools -- |g 13.4. |t Data Transformation Tools -- |g 13.5. |t Sampling and Missing Value Tools -- |g 13.6. |t Optimization Tools. |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Consumer behavior. | |
| 650 | 4 | |a Minería de datos. |9 59748 | |
| 650 | 7 | |a Consumer behavior. |2 fast |0 (OCoLC)fst00876238 | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Data Mining |2 gnd | |
| 650 | 7 | |a RapidMiner |2 gnd |9 59749 | |
| 650 | 7 | |a Datenanalyse |2 gnd |9 59750 | |
| 650 | 7 | |a Prognoseverfahren |2 gnd |9 59751 | |
| 700 | 1 | |a Deshpande, Balachandre, |e author. |9 59752 | |
| 856 | 4 | 1 | |u http://londonmet.eblib.com/patron/FullRecord.aspx?p=1875324 |z E-book - Full text from ebookcentral |
| 955 | |a pc27 2014-10-01 |a rl06 2017-02-03 to SMA | ||
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