Search Results - (((((((ant OR kkantor) OR wiant) OR data) OR cantor) OR anne) OR halted) OR antiii) algorithms.

Data mining techniques : for marketing, sales, and customer relationship management by Berry, Michael J. A.

Table of Contents: “…Why and what is data mining? -- The virtuous cycle of data mining -- Data mining methodology and best practices -- Data mining applications in marketing and customer relationship management -- The lure of statistics : data mining using familiar tools -- Decision trees -- Artificial neural networks -- Nearest neighbor approaches : memory-based reasoning and collaborative filtering -- Market basket analysis and association rules -- Link analysis -- Automatic cluster detection -- Knowing when to worry : hazard functions and survival analysis in marketing -- Genetic algorithms -- Data mining throughout the customer life cycle -- Data warehousing, OLAP, and data mining -- Building the data mining environment -- Preparing data for mining -- Putting data mining to work.…”
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Music data mining

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Data mining techniques : for marketing, sales, and customer relationship management by Linoff, Gordon, Berry, Michael J. A.

Table of Contents: “…What Is data mining and why do it? -- Data mining applications in marketing and customer relationship management -- The data mining process -- Statistics 101: What you should know about data -- Descriptions and prediction: profiling and predictive modeling -- Data mining using classic statistical techniques -- Decision trees -- Artificail neural networks -- Nearest neighbor approaches: Memory-based reasoning and collaborative filtering -- Knowing when to worry: Using survival analysis to understand customers -- Genetic algorithms and swarm intelligence -- Tell me something new: Pattern discovery and data mining -- Finding islands of similarity: Automatic cluster detection -- Alternative approaches to cluster detection -- Market basket analysis and association rules -- Link analysis -- Data warehousing, OLAP, analytic sandboxes, and data mining -- Building customer signatures -- Derived variables: Making the data mean more -- Too much of a good thing? …”
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Data mining in finance : advances in relational and hybrid methods by Kovalerchuk, Boris

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Principles of data integration by Doan, AnHai

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Practical data analysis by Cuesta, Hector

Table of Contents: “…Numeric facetsTransforming data; Exporting data; Operation history; Summary; Chapter 3:Data Visualization; Data-Driven Documents (D3); HTML; DOM; CSS; JavaScript; SVG; Getting started with D3.js; Bar chart; Pie chart; Scatter plot; Single line chart; Multi-line chart; Interaction and animation; Summary; Chapter 4:Text Classification; Learning and classification; Bayesian classification; Naïve Bayes algorithm; E-mail subject line tester; The algorithm; Classifier accuracy; Summary; Chapter 5:Similarity-based Image Retrieval; Image similarity search; Dynamic time warping (DTW).…”
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Pharmaceutical data mining : approaches and applications for drug discovery

Table of Contents: “…Livingstone and John Bradshaw -- Drug gold and data dragons : myths and realities of data mining in the pharmaceutical industry / Barry Robson and Andy Vaithiligam -- Application of data mining algorithms in pharmaceutical research and development / Konstantin V. …”
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Data and Application Security : Developments and Directions by Thuraisingham, Bhavani M.

Table of Contents: “…: A Java-Based System For XML Data Protection -- A Fair-Exchange E-Commerce Protocol With Automated Dispute Resolution -- XML Access Control Systems: A Component-Based Approach -- A Configurable Security Architecture Prototype -- Distributed Policies for Data Management -- Making Policies Mobile -- Security Architecture of The Multimedia Mediator -- Simulation And Analysis of Cryptographic Protocols -- Authentic Third-Party Data Publication -- Protecting File Systems Against Corruption Using Checksums -- Web Security and Privacy -- Coordinating Policy for Federated Applications -- Integrating Multilevel Security Policies in Multilevel Federated Database Systems -- Wrappers -- A Mechanism to Support State-Based Authorization in Web Applications -- An Integrated Framework for Database Privacy Protection -- Discovery of Multi-Level Security Policies -- Protecting Deductive Databases from Unauthorized Retrievals -- Confidentiality vs Integrity in Secure Databases -- Extending SQL's Grant Operation to Limit Privileges -- Language Extensions for Programmable Security -- Protecting Privacy from Continuous High-Resolution Satellite Surveillance -- Database Security Integration Using Role-Based Access Control -- User Role-Based Security Model for a Distributed Environment -- WorkFlow Analyzed for Security and Privacy in using Databases -- Identifying Security Holes in OLAP Applications -- Algorithms and Experience in Increasing the Intelligibility and Hygiene of Access Control in Large Organizations -- Database Security 2000 -- Declarative Semantics of Belief Queries in Mls Deductive Databases -- Trust Management in Distributed Databases -- Active Authorization as High-level Control -- Conference Key Agreement Protocol using Oblivious Transfer -- An Integration Model of Role-Based Access Control and Activity-Based Access Control Using Task -- Authorization Model in Object-Oriented Systems -- Panel 2 -- Conference Summary.…”
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Big data analytics : turning big data into big money by Ohlhorst, Frank, 1964-

Table of Contents: “…Processing PowerChoosing among In-house, Outsourced, or Hybrid Approaches; Chapter 7: Security, Compliance, Auditing, and Protection; Pragmatic Steps to Securing Big Data; Classifying Data; Protecting Big Data Analytics; Big Data and Compliance; The Intellectual Property Challenge; Chapter 8: The Evolution of Big Data; Big Data: The Modern Era; Today, Tomorrow, and the Next Day; Changing Algorithms; Chapter 9: Best Practices for Big Data Analytics; Start Small with Big Data; Thinking Big; Avoiding Worst Practices; Baby Steps; The Value of Anomalies; Expediency versus Accuracy.…”
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Smart data pricing

Table of Contents: “…4.1 Introduction4.2 Pricing Under Demand Flexibilities -- 4.3 Pricing Under Predictable Demand -- References -- Chapter 5: Dual Pricing Algorithms by Wireless Network Duality for Utility Maximization -- 5.1 Introduction -- 5.2 Utility Maximization -- 5.3 The Wireless Network Duality -- 5.4 Numerical Examples -- 5.5 Conclusion -- References -- Chapter 6: Human Factors in Smart Data Pricing -- 6.1 Introduction -- 6.2 Methodology -- 6.3 Hci Lessons From the Energy Market -- 6.4 User Psychology in Home Networks -- 6.5 User Psychology in Bandwidth Pricing…”
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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…11.2 Kohonen Networks -- 11.2.1 Kohonen Networks Algorithm -- 11.3 Example of a Kohonen Network Study -- 11.4 Cluster Validity -- 11.5 Application of Clustering Using Kohonen Networks -- 11.6 Interpreting the Clusters -- 11.6.1 Cluster Profiles -- 11.7 Using Cluster Membership as Input to Downstream Data Mining Models -- THE R ZONE -- References -- Exercises -- Hands-On Analysis -- 12 Association Rules -- 12.1 Affinity Analysis and Market Basket Analysis -- 12.1.1 Data Representation for Market Basket Analysis -- 12.2 Support, Confidence, Frequent Itemsets, and the a Priori Property -- 12.3 How Does the a Priori Algorithm Work? …”
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Data mining and warehousing by Prabhu, S.

Table of Contents: “…13; -- 2.3 Anatomy of Data Mining13; -- 2.4 Different Types of Knowledge13; -- Exercise13; -- Chapter 3 Knowledge Discovery Process -- 3.1 Introduction13; -- 3.2 Evaluation of Data Mining13; -- 3.3 Stages of the Data Mining Process13; -- 3.4 Data Mining Operations13; -- 3.5 Architecture of Data Mining13; -- Exercise13; -- Chapter 4 Data Mining Techniques -- 4.1 Introduction13; -- 4.2 Classification13; -- 4.3 Neural Networks13; -- 4.4 Decision Trees13; -- 4.5 Genetic Algorithm13; -- 4.6 Clustering13; -- 4.7 Online Analytic Processing (OLAP)13; -- 4.8 Association Rules13; -- 4.9 Emerging Trends in Data Mining13; -- 4.10 Data Mining Research Projects13; -- Exercise13; -- Chapter 5 Real Time Applications and Future Scope -- 5.1 Applications of Data Mining13; -- 5.2 Future Scope13; -- 5.3 Data Mining Products13; -- Exercise13; -- Chapter 6 Data Warehouse Evaluation -- 6.1 The Calculations for Memory Capacity13; -- 6.2 Data, Information and Knowledge13; -- 6.3 Fundamental of Database13; -- 6.4 OLAP And OLAP Server13; -- 6.5 Data Warehouses, OLTP, OLAP and Data Mining13; -- Exercise13; -- Chapter 7 Data Warehouse Design -- 7.1 Introduction13; -- 7.2 The Central Data Warehouse13; -- 7.3 Data Warehousing Objects13; -- 7.4 Goals of Data Warehouse Architecture13; -- 7.5 Data Warehouse Users13; -- 7.6 Design the Relational Database and OLAP Cubes13; -- 7.7 Data Warehousing Schemas13; -- Exercise13; -- Chapter 8 Partitioning in Data Warehouse -- 8.1 Introduction13; -- 8.2 Hardware Partitioning13; -- 8.3 RAID Levels13; -- 8.4 Software Partitioning Methods13; -- Exercise13; -- Chapter 9 Data Mart and Meta Data -- 9.1 Introduction13; -- 9.2 Data Mart13; -- 9.3 Meta Data13; -- 9.4 Legacy Systems13; -- Exercise13; -- Chapter 10 Backup and Recovery of the Data Warehouse -- 10.1 Introduction13; -- 10.2 Types of Backup13; -- 10.3 Backup the Data Warehouse13; -- 10.4 Data Warehouse Recovery Models13; -- Exercise13; -- Chapter 11 Performance Tuning and Future of data Warehouse -- 11.1 Introduction13; -- 11.2 Prioritized Tuning Steps13; -- 11.3 Challenges of the Data Warehouse13; -- 11.4 Benefits of Data Warehousing13; -- 11.5 Future of the Data Warehouse13; -- 11.6 New Architecture of Data Warehouse13; -- Exercise13; -- Appendix A Glossary -- Appendix B Multiple Choice Questions13; -- Appendix C Questions & Answers13; -- Appendix D Model Question Papers13; -- Bibliography -- Index.…”
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Missing Data Methods : Cross-Sectional Methods and Applications. by Drukker, David M.

Table of Contents: “…NotesAcknowledgment; References; Orthogonality in the single index model; On the estimation of selection models when participation is endogenous and misclassified; Introduction; The model and estimator; Sampling algorithm; Simulated data example; Summary and conclusions; Notes; Acknowledgments; References; summary tables for additional simulations; Process for simulating non -- normal errors; Efficient probit estimation with partially missing covariates; Introduction; Model Specification; Efficient estimators and variances; Testing assumptions and possible modifications; Other models.…”
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Data analytics for pandemics : a COVID-19 case study by Shinde, Gitanjali Rahul, 1983-

Table of Contents: “…Big Data Analytics for COVID-19 <BR> 3.1 All You Need to Know<BR> 3.2 Big Data Analytics and COVID-19<BR>3.2.1 Statistical Parameters<BR>3.2.2 Predictive Analytics<BR> 3.3 Data Modelling and Performance <BR>3.3.1 Data Modelling Phases<BR>3.3.2 Ensemble Data Model<BR>3.3.3 Model Performance<BR> 3.4 Big Data Technique <BR>3.4.1 Association Rule Learning<BR>3.4.2 Classification Tree Analysis<BR>3.4.3 Genetic Algorithm<BR>3.4.4 Machine Learning<BR>3.4.5 Regression Analysis<BR>3.4.6 Social Network Analysis<BR> 3.5 Big Data Tools and Technology<BR> <BR>4. …”
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Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language by Fischetti, Tony

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Advances in data science : symbolic, complex, and network data

Table of Contents: “…Hard clustering with automated weighting of tables and variables 196 -- 9.4.1. Clustering algorithms MND–W and MND–WT 196 -- 9.5. Applications: UCI data sets 201 -- 9.5.1. …”
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Advanced data structures : theory and applications by Saha, Suman, Shukla, S. K. (Shailendra Kumar), 1969-

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Mathematica data visualization : create and prototype interactive data visualizations using Mathematica by Saquib, Nazmus

Table of Contents: “…Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Visualization as a Tool to Understand Data; The importance of visualization; Types of datasets; Tables; Scalar fields; Time series; Graphs; Text; Cartographic data; Mathematica as a tool for visualization; Getting started with Mathematica; Creating and selecting cells; Evaluating a cell; Suppressing output from a cell; Cell formatting; Commenting; Aborting evaluation; Upcoming chapters; Further reading; Summary; Chapter 2: Dissecting Data Using Mathematica.…”
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Data Protection and Privacy : the Internet of Bodies. by Leenes, Ronald

Table of Contents: “…Machine Learning: Social Reconstruction and the Self; 2. Algorithmic Inequalities; 3. The Data Protection Toolbox: The Search for the Collective; 4. …”
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Fuzzy systems and data mining III : proceedings of FSDM 2017

Table of Contents: “…Multiple Alignments of Data Objects and Generalized Center Star Algorithm /…”
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