Autore	Witten I. H (Ian H.)
Edizione	[3rd ed.]
Pubbl/distr/stampa	Amsterdam, : Elsevier/Morgan Kaufmann, 2011
Descrizione fisica	1 online resource (665 p.) : ill
Disciplina	006.3/12
Altri autori (Persone)	FrankEibe HallMark A
Collana	The Morgan Kaufmann Series in Data Management Systems
Soggetto topico	Data mining
ISBN	1-282-95388-5 9786612953880 0-08-089036-9
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Part I. Machine learning tools and techniques: 1. What's it all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.
Record Nr.	UNINA-9910785305703321

Autore	Cichosz Pawel
Pubbl/distr/stampa	Chichester, England : , : Wiley, , 2015
Descrizione fisica	1 online resource (718 pages) : illustrations
Disciplina	006.3/12
Soggetto topico	Data mining Computer algorithms R (Computer program language)
ISBN	1-118-95084-4 1-118-95095-X 1-118-33258-X
Classificazione	MAT029000
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Part I. Preliminaries -- 1. Tasks -- 2. Basic statistics -- Part II. Classification -- 3. Decision trees -- 4. Naèive Bayes classifier -- 5. Linear classification -- 6. Misclassification costs -- 7. Classification model evaluation -- Part III. Regression -- 8. Linear regression -- 9. Regression trees -- 10. Regression model evaluation -- Part IV. Clustering -- 11. (Dis)similarity measures -- 12. k-Centers clustering -- 13. Hierarchical clustering -- 14. Clustering model evaluation -- Part V. Getting better models -- 15. Model ensembles -- 16. Kernel methods -- 17. Attribute transformation -- 18. Discretization -- 19. Attribute selection.
Record Nr.	UNINA-9910208955403321

Pubbl/distr/stampa	Hauppauge, N.Y., : Nova Science Publisher's, c2010
Descrizione fisica	1 online resource (329 p.)
Disciplina	006.3/12
Altri autori (Persone)	SpendlerLawrence I
Collana	Computer Science, Technology and Applications
Soggetto topico	Data mining Computer algorithms
Soggetto genere / forma	Electronic books.
ISBN	1-61470-797-9
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	""DATA MINING AND MANAGEMENT""; ""DATA MINING AND MANAGEMENT ""; ""CONTENTS ""; ""PREFACE ""; ""COGNITIVE FINANCE: DATA ANALYSIS WITH A BEHAVIORAL EDGE""; ""Abstract""; ""Introduction""; ""Context Specific Strategy Usage""; ""Changes in Strategies""; ""Behavioral Finance""; ""Methods for Capturing Cognitive Processes""; ""Spending Strategies""; ""Behavioral Evaluation""; ""Use of Behavioral Data""; ""Saving Strategies""; ""Saving Concept Study""; ""Saving Differences Study""; ""Saving Solutions""; ""Conclusion""; ""Characterizing Mental Processes""; ""Financial Personality"" ""Cognitive Approaches to Finance""""References""; ""THE DATA MINING PERSPECTIVE OF THE INDIAN MINERAL INDUSTRY""; ""Abstract""; ""1.0. Introduction""; ""1.1. Preamble""; ""Aim And Scope of this Chapter""; ""1.2. An Overview of Mineral Production in India""; ""1.4. Key Issues of the Mineral Industry""; ""1.4. Impacts of Mineral Industries""; ""1.5. Necessity of EIA Studies""; ""2.0. Impacts of Coal Mining Projects""; ""2.1. Impacts of Coal Mining on Air""; ""2.2. Impacts of Coal Mining on Water""; ""2.3. Impacts of Coal Mining on Land""; ""2.4. Impact of Coal Mining on Noise"" ""2.5. Impacts of Mining on Socioeconomic Aspects""""2.6. EIA Studies for Coal Mining Projects""; ""3.0. Impacts of Coal Washery Projects""; ""3.1. Impacts of Coal Washery Projects on Air Environment""; ""3.2. Impacts of Coal Washery Projects on Water Environment""; ""3.3. Impacts of Coal Washery Projects on Socioeconomic Environment""; ""3.4. EIA Studies for Coal Washery Projects""; ""4.0. Impacts of Coke Plants""; ""4.1. Impacts of Coke Plants on Air Environment""; ""4.2. Impacts of Coke Plant on Water Environment""; ""5.0. Impacts due to the Power Sector"" ""6.0. Impacts of Iron Ore Benificiation Plants""""7.0. Impacts of Copper Ore Benificiation Plants""; ""8.0. Impacts of Small-Scale Mines""; ""9.0. Data Mining of the Mineral Industry""; ""9.1. Assessment of Impacts due to Coal Mining""; ""9.1.1. Assessment of Impacts on Air Environment""; ""Recommendations""; ""9.1.2. Assessment of Impacts on Water Environment""; ""9.1.3. Assessment of Impacts on Land Environment""; ""9.1.4. Impact of Coal Mining on Noise Environment""; ""9.1.5. Impacts on Socioeconomic Environment""; ""9.1.6. EIA Studies for Coal Mining Projects"" ""9.2. Assessment of Impacts due to Coal Washery Projects""""9.2.1. Impacts of Coal Washery Projects on Air Environment""; ""9.2.2. Assessment of Impacts of Coal Washery Projects on Water""; ""9.2.3. Impacts of Coal Washery Projects on Socioeconomic Environment""; ""9.2.4. EIA Studies for Coal Washery Projects""; ""9.3. Assessment of Impacts due to Coke Plants""; ""9.3.1. Impacts of Coke Plants on Air Environment""; ""9.3.2. Impacts of Coke Plant on Water Environment""; ""9.4. Assessment of Impacts due to Power Sector""; ""9.5. Assessment of Impacts due to Iron Ore Beneficiation Plants"" ""9.6. Assessment of Impacts due Copper Ore Beneficiation Plants""
Record Nr.	UNINA-9910456946103321

Autore	Larose Daniel T.
Edizione	[Second edition.]
Pubbl/distr/stampa	Hoboken, New Jersey : , : John Wiley & Sons, , 2015
Descrizione fisica	1 online resource (827 p.)
Disciplina	006.3/12
Collana	Wiley Series on Methods and Applications in Data Mining
Soggetto topico	Data mining Prediction theory
Soggetto genere / forma	Electronic books.
ISBN	1-118-86870-6 1-118-86867-6
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover; Contents; Preface; Acknowledgments; Part I Data Preparation; Chapter 1 An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need for Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.4.1 CRISP-DM: The Six Phases; 1.5 Fallacies of Data Mining; 1.6 What Tasks Can Data Mining Accomplish; 1.6.1 Description; 1.6.2 Estimation; 1.6.3 Prediction; 1.6.4 Classification; 1.6.5 Clustering; 1.6.6 Association; The R Zone; R References; Exercises Chapter 2 Data Preprocessing2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data; 2.4 Identifying Misclassifications; 2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables 2.17 Adding an Index Field2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed; 2.20 Removal of Duplicate Records; 2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3 Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know the Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields 3.8 Binning Based on Predictive Value3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables; 3.11 Using EDA to Investigate Correlated Predictor Variables; 3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4 Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.4.1 The Eigenvalue Criterion; 4.4.2 The Proportion of Variance Explained Criterion; 4.4.3 The Minimum Communality Criterion 4.4.4 The Scree Plot Criterion4.5 Profiling the Principal Components; 4.6 Communalities; 4.6.1 Minimum Communality Criterion; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites; 4.12 An Example of a User-Defined Composite; The R Zone; R References; Exercises; Part II Statistical Analysis; Chapter 5 Univariate Statistical Analysis; 5.1 Data Mining Tasks in Discovering Knowledge in Data; 5.2 Statistical Approaches to Estimation and Prediction; 5.3 Statistical Inference 5.4 How Confident are We in Our Estimates?
Record Nr.	UNINA-9910460169903321

Autore	Tuffery Stéphane
Edizione	[1st ed.]
Pubbl/distr/stampa	Chichester, West Sussex ; ; Hoboken, NJ., : Wiley, 2011
Descrizione fisica	1 online resource (717 p.)
Disciplina	006.3/12
Collana	Wiley series in computational statistics
Soggetto topico	Data mining Statistical decision
ISBN	1-283-37397-1 9786613373977 0-470-97928-3 0-470-97916-X 0-470-97917-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Data Mining and Statistics for Decision Making; Contents; Preface; Foreword; Foreword from the French language edition; List of trademarks; 1 Overview of data mining; 1.1 What is data mining?; 1.2 What is data mining used for?; 1.2.1 Data mining in different sectors; 1.2.2 Data mining in different applications; 1.3 Data mining and statistics; 1.4 Data mining and information technology; 1.5 Data mining and protection of personal data; 1.6 Implementation of data mining; 2 The development of a data mining study; 2.1 Defining the aims; 2.2 Listing the existing data; 2.3 Collecting the data 2.4 Exploring and preparing the data2.5 Population segmentation; 2.6 Drawing up and validating predictive models; 2.7 Synthesizing predictive models of different segments; 2.8 Iteration of the preceding steps; 2.9 Deploying the models; 2.10 Training the model users; 2.11 Monitoring the models; 2.12 Enriching the models; 2.13 Remarks; 2.14 Life cycle of a model; 2.15 Costs of a pilot project; 3 Data exploration and preparation; 3.1 The different types of data; 3.2 Examining the distribution of variables; 3.3 Detection of rare or missing values; 3.4 Detection of aberrant values 3.5 Detection of extreme values3.6 Tests of normality; 3.7 Homoscedasticity and heteroscedasticity; 3.8 Detection of the most discriminating variables; 3.8.1 Qualitative, discrete or binned independent variables; 3.8.2 Continuous independent variables; 3.8.3 Details of single-factor non-parametric tests; 3.8.4 ODS and automated selection of discriminating variables; 3.9 Transformation of variables; 3.10 Choosing ranges of values of binned variables; 3.11 Creating new variables; 3.12 Detecting interactions; 3.13 Automatic variable selection; 3.14 Detection of collinearity; 3.15 Sampling 3.15.1 Using sampling3.15.2 Random sampling methods; 4 Using commercial data; 4.1 Data used in commercial applications; 4.1.1 Data on transactions and RFM Data; 4.1.2 Data on products and contracts; 4.1.3 Lifetimes; 4.1.4 Data on channels; 4.1.5 Relational, attitudinal and psychographic data; 4.1.6 Sociodemographic data; 4.1.7 When data are unavailable; 4.1.8 Technical data; 4.2 Special data; 4.2.1 Geodemographic data; 4.2.2 Profitability; 4.3 Data used by business sector; 4.3.1 Data used in banking; 4.3.2 Data used in insurance; 4.3.3 Data used in telephony; 4.3.4 Data used in mail order 5 Statistical and data mining software5.1 Types of data mining and statistical software; 5.2 Essential characteristics of the software; 5.2.1 Points of comparison; 5.2.2 Methods implemented; 5.2.3 Data preparation functions; 5.2.4 Other functions; 5.2.5 Technical characteristics; 5.3 The main software packages; 5.3.1 Overview; 5.3.2 IBM SPSS; 5.3.3 SAS; 5.3.4 R; 5.3.5 Some elements of the R language; 5.4 Comparison of R, SAS and IBM SPSS; 5.5 How to reduce processing time; 6 An outline of data mining methods; 6.1 Classification of the methods; 6.2 Comparison of the methods; 7 Factor analysis 7.1 Principal component analysis
Record Nr.	UNINA-9910130875003321