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010 a| 2014952930;

015 a| GBB4C23252| bnb;

015 a| GBB4C42522| bnb;

016 7 a| 0169311962| Uk;

016 7 a| 0169331232| Uk;

020 a| 9780128014608 (pbk.);

020 a| 0128014601 (pbk.);

020 a| 9780128016503;

020 a| 0128016507;

035 a| (WaSeSS)ssj0001437513;

042 a| lccopycat;

049 a| EREEa| NEHH;

050 00 a| QA76.9.D343b| K68 2015;

082 04 a| 006.312 K87 2015;

100 1 a| Kotu, Vijay.=| ^A1261454;

245 10 a| Predictive analytics and data miningh| [electronic resource] :b| concepts and practice with RapidMiner /c| Vijay Kotu, Bala Deshpande, PhD.;

260 a| Amsterdam :b| Elsevier/Morgan Kaufmann, Morgan Kaufmann is an imprint of Elsevier,c| [2015];

300 a| xix, 425 pages :b| illustrations ;c| 24 cm;

504 a| Includes bibliographical references and index.;

505 00 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.;

506 a| Available only to authorized users.;

538 a| Mode of access: World Wide Web;

650 0 a| Data mining.=| ^A408868;

650 0 a| Consumer behavior.=| ^A231577;

650 4 a| Minería de datos.;

650 7 a| Consumer behavior.2| fast0| (OCoLC)fst00876238;

650 7 a| Data mining.2| fast0| (OCoLC)fst00887946;

650 7 a| Data Mining2| gnd;

650 7 a| RapidMiner2| gnd;

650 7 a| Datenanalyse2| gnd;

650 7 a| Prognoseverfahren2| gnd;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Deshpande, Balachandre.=| ^A1378823;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=1875324;

856 40 z| Full text available from eBook - Computer Science 2014 [EBCCS14]u| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Fbook%2F9780128014608;

947 a| (OCoLC)ocn900080446;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 4987228;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4987228-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 12/8/2018x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4987228-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 12/8/2018x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4987228-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 12/8/2018x| EBOOKz| JERESOURCE;

Predictive analytics and data mining concepts and practice with RapidMiner / Vijay Kotu, Bala Deshpande, PhD.

Author/creator	Kotu, Vijay
Other author	Deshpande, Balachandre.
Format	Electronic
Publication Info	Amsterdam : Elsevier/Morgan Kaufmann, Morgan Kaufmann is an imprint of Elsevier, [2015]
Description	xix, 425 pages : illustrations ; 24 cm
Supplemental Content	Full text available from Ebook Central - Academic Complete
Supplemental Content	Full text available from eBook - Computer Science 2014 [EBCCS14]
Subjects	Data mining. Consumer behavior. Minería de datos.

Contents	Machine generated contents note: 1.1. What Data Mining Is -- 1.2. What Data Mining Is Not -- 1.3. Case for Data Mining -- 1.4. Types of Data Mining -- 1.5. Data Mining Algorithms -- 1.6. Roadmap for Upcoming Chapters -- 2.1. Prior Knowledge -- 2.2. Data Preparation -- 2.3. Modeling -- 2.4. Application -- 2.5. Knowledge -- 3.1. Objectives of Data Exploration -- 3.2. Data Sets -- 3.3. Descriptive Statistics -- 3.4. Data Visualization -- 3.5. Roadmap for Data Exploration -- 4.1. Decision Trees -- 4.2. Rule Induction -- 4.3. k-Nearest Neighbors -- 4.4. Naïve Bayesian -- 4.5. Artificial Neural Networks -- 4.6. Support Vector Machines -- 4.7. Ensemble Learners -- 5.1. Linear Regression -- 5.2. Logistic Regression -- 6.1. Concepts of Mining Association Rules -- 6.2. Apriori Algorithm -- 6.3. FP-Growth Algorithm -- 7.1. Types of Clustering Techniques -- 7.2. k-Means Clustering -- 7.3. DBSCAN Clustering -- 7.4. Self-Organizing Maps -- 8.1. Confusion Matrix (or Truth Table) -- 8.2. Receiver Operator Characteristic (ROC) Curves and Area under the Curve (AUC) -- 8.3. Lift Curves -- 8.4. Evaluating the Predictions: Implementation -- 9.1. How Text Mining Works -- 9.2. Implementing Text Mining with Clustering and Classification -- 10.1. Data-Driven Approaches -- 10.2. Model-Driven Forecasting Methods -- 11.1. Anomaly Detection Concepts -- 11.2. Distance-Based Outlier Detection -- 11.3. Density-Based Outlier Detection -- 11.4. Local Outlier Factor -- 12.1. Classifying Feature Selection Methods -- 12.2. Principal Component Analysis -- 12.3. Information Theory-Based Filtering for Numeric Data -- 12.4. Chi-Square-Based Filtering for Categorical Data -- 12.5. Wrapper-Type Feature Selection -- 13.1. User Interface and Terminology -- 13.2. Data Importing and Exporting Tools -- 13.3. Data Visualization Tools -- 13.4. Data Transformation Tools -- 13.5. Sampling and Missing Value Tools -- 13.6. Optimization Tools.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2014952930
ISBN	9780128014608 (pbk.)
ISBN	0128014601 (pbk.)
ISBN	9780128016503
ISBN	0128016507

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Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Online Content Full text available from Ebook Central - Academic Complete Full text available from eBook - Computer Science 2014 [EBCCS14]	✔ Available

Predictive analytics and data mining concepts and practice with RapidMiner / Vijay Kotu, Bala Deshpande, PhD.

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