Search Results - (((((((ant OR arts) OR vikan) OR data) OR cantor) OR anne) OR walted) OR wanting) algorithms.

Data Mining : a Tutorial-Based Primer, Second Edition. by Roiger, Richard J.

Table of Contents: “…3.2 A BASIC COVERING RULE ALGORITHM3.3 GENERATING ASSOCIATION RULES; 3.3.1 Confidence and Support; 3.3.2 Mining Association Rules: An Example; 3.3.3 General Considerations; 3.4 THE K-MEANS ALGORITHM; 3.4.1 An Example Using K-means; 3.4.2 General Considerations; 3.5 GENETIC LEARNING; 3.5.1 Genetic Algorithms and Supervised Learning; 3.5.2 General Considerations; 3.6 CHOOSING A DATA MINING TECHNIQUE; 3.7 CHAPTER SUMMARY; 3.8 KEY TERMS; Section II: Tools for Knowledge Discovery; Chapter 4. …”
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Data Mining.

Table of Contents: “…-- Decision Tree Induction -- GINI Index -- Entropy -- Misclassification Measure -- Practical Issues Regarding Decision Trees -- Predictive Accuracy -- STOP Condition for Split -- Pruning Decision Trees -- Extracting Classification Rules from Decision Trees -- Advantages of Decision Trees -- Data Mining Techniques and Models -- Data Mining Methods -- Bayesian Classifier -- Artificial Neural Networks -- Perceptron -- Types of Artificial Neural Networks -- Probabilistic Neural Networks -- Some Neural Networks Applications -- Support Vector Machines -- Association Rule Mining -- Rule-Based Classification -- k-Nearest Neighbor -- Rough Sets -- Clustering -- Hierarchical Clustering -- Non-hierarchical/Partitional Clustering -- Genetic Algorithms -- Components of GAs -- Architecture of GAs -- Applications -- Classification Performance Evaluation -- Costs and Classification Accuracy -- ROC (Receiver Operating Characteristic) Curve -- Statistical Methods for Comparing Classifiers -- References -- Index.…”
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Data Science : The Executive Summary - a Technical Book for Non-Technical Professionals. by Cady, Field

Table of Contents: “…2.1.5 What Data Scientists Don't (Necessarily) Do -- 2.1.5.1 Working Without Data -- 2.1.5.2 Working with Data that Can't Be Interpreted -- 2.1.5.3 Replacing Subject Matter Experts -- 2.1.5.4 Designing Mathematical Algorithms -- 2.2 Data Science in an Organization -- 2.2.1 Types of Value Added -- 2.2.1.1 Business Insights -- 2.2.1.2 Intelligent Products -- 2.2.1.3 Building Analytics Frameworks -- 2.2.1.4 Offline Batch Analytics -- 2.2.2 One-Person Shops and Data Science Teams -- 2.2.3 Related Job Roles -- 2.2.3.1 Data Engineer -- 2.2.3.2 Data Analyst -- 2.2.3.3 Software Engineer…”
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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…143</p> <p>5.2.3HDFS Architecture. 143</p> <p>5.2.4HDFS Read/Write Operation. 146</p> <p>5.2.5Rack Awareness. 148</p> <p>5.2.6Features of HDFS. 149</p> <p>5.2.6.1Cost-effective. 149</p> <p>5.2.6.2Distributed storage. 149</p> <p>5.2.6.3Data Replication. 149</p> <p>5.3 Hadoop Computation. 149</p> <p>5.3.1MapReduce. 149</p> <p>5.3.1.1Mapper. 151</p> <p>5.3.1.2Combiner. 151</p> <p>5.3.1.3 Reducer. 152</p> <p>5.3.1.4 JobTracker and TaskTracker. 153</p> <p>5.3.2 MapReduce Input Formats. 154</p> <p>5.3.3 MapReduce Example. 156</p> <p>5.3.4 MapReduce Processing. 157</p> <p>5.3.5 MapReduce Algorithm.. 160</p> <p>5.3.6 Limitations of MapReduce. 161</p> <p>5.4Hadoop 2.0. 161</p> <p>5.4.1Hadoop 1.0 limitations. 162</p> <p>5.4.2 Features of Hadoop 2.0. 163</p> <p>5.4.3 Yet Another Resource Negotiator (YARN). 164</p> <p>5.4.3 Core components of YARN.. 165</p> <p>5.4.3.1 ResourceManager. 165</p> <p>5.4.3.2 NodeManager. 166</p> <p>5.4.4 YARN Scheduler. 169</p> <p>5.4.4.1 <i>FIFO scheduler</i>. 169</p> <p>5.4.4.2 <i>Capacity Scheduler</i>. 170</p> <p>5.4.4.3 <i>Fair Scheduler</i>. 170</p> <p>5.4.5 Failures in YARN.. 171</p> <p>5.4.5.1ResourceManager failure. 171</p> <p>5.4.5.2 ApplicationMaster failure. 172</p> <p>5.4.5.3 NodeManagerFailure. 172</p> <p>5.4.5.4 Container Failure. 172</p> <p>5.3 HBASE. 173</p> <p>5.4 Apache Cassandra. 176</p> <p>5.5 SQOOP. 177</p> <p>5.6 Flume. 179</p> <p>5.6.1 Flume Architecture. 179</p> <p>5.6.1.1 Event. 180</p> <p>5.6.1.2 Agent. 180</p> <p>5.7 Apache Avro. 181</p> <p>5.8 Apache Pig. 182</p> <p>5.9 Apache Mahout. 183</p> <p>5.10 Apache Oozie. 183</p> <p>5.10.1 Oozie Workflow.. 184</p> <p>5.10.2 Oozie Coordinators. 186</p> <p>5.10.3 Oozie Bundles. 187</p> <p>5.11 Apache Hive. 187</p> <p>5.11 Apache Hive. 187</p> <p>Hive Architecture. 189</p> <p>Hadoop Distributions. 190</p> <p>Chapter 5refresher. 191</p> <p>Conceptual short questions with answers. 194</p> <p>Frequently asked Interview Questions. 199</p> <p>Chapter Objective. 200</p> <p>6.1 Terminologies of Big Data Analytics. 201</p> <p><i>Data Warehouse</i>. 201</p> <p><i>Business Intelligence</i>. 201</p> <p><i>Analytics</i>. 202</p> <p>6.2 Big Data Analytics. 202</p> <p>6.2.1 Descriptive Analytics. 204</p> <p>6.2.2 Diagnostic Analytics. 205</p> <p>6.2.3 Predictive Analytics. 205</p> <p>6.2.4 Prescriptive Analytics. 205</p> <p>6.3 Data Analytics Lifecycle. 207</p> <p>6.3.1 Business case evaluation and Identify the source data. 208</p> <p>6.3.2 Data preparation. 209</p> <p>6.3.3 Data Extraction and Transformation. 210</p> <p>6.3.4 Data Analysis and visualization. 211</p> <p>6.3.5 Analytics application. 212</p> <p>6.4 Big Data Analytics Techniques. 212</p> <p>6.4.1 Quantitative Analysis. 212</p> <p>6.4.3 Statistical analysis. 214</p> <p>6.4.3.1 A/B testing. 214</p> <p>6.4.3.2 Correlation. 215</p> <p>6.4.3.3 Regression. 218</p> <p>6.5 Semantic Analysis. 220</p> <p>6.5.1 Natural Language Processing. 220</p> <p>6.5.2 Text Analytics. 221</p> <p>6.7 Big Data Business Intelligence. 222</p> <p>6.7.1 Online Transaction Processing (OLTP). 223</p> <p>6.7.2 Online Analytical Processing (OLAP). 223</p> <p>6.7.3 Real-Time Analytics Platform (RTAP). 224</p> <p>6.6Big Data Real Time Analytics Processing. 225</p> <p>6.7 Enterprise Data Warehouse. 227</p> <p>Chapter 6 Refresher. 228</p> <p>Concept…”
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Big data : un changement de paradigme peut en cacher un autre.

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Computer animation : algorithms and techniques by Parent, Rick

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Error Correction Coding : Mathematical Methods and Algorithms. by Moon, Todd K.

Table of Contents: “…Cover -- Title Page -- Copyright -- Contents -- Preface -- List of Program Files -- List of Laboratory Exercises -- List of Algorithms -- List of Figures -- List of Tables -- List of Boxes -- About the Companion Website -- Part I Introduction and Foundations -- Chapter 1 A Context for Error Correction Coding -- 1.1 Purpose of This Book -- 1.2 Introduction: Where Are Codes? …”
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Microscope Image Processing. by Wu, Qiang, 1958-

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Algorithms, architectures and information systems security

Table of Contents: “…Theory of a Practical Delaunay Meshing Algorithm for a Large Class of Domains S.-W. Cheng, T.K. …”
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Body sensor networking, design, and algorithms by Sanei, Saeid, Jarchi, Delaram, 1983-, Constantinides, A. G.

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Algorithmic Graph Theory and Perfect Graphs. by Rheinboldt, Werner

Table of Contents: “…The Design of Efficient Algorithms; 1. The Complexity of Computer Algorithms; 2. …”
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Network and discrete location : models, algorithms, and applications by Daskin, Mark S., 1952-

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Data intensive computing applications for big data

Table of Contents: “…Application of Big Data Analytics in Cloud Computing via Machine LearningA Novel Mechanism for Cloud Data Management in Distributed Environment; Spark SQL with Hive Context or SQL Context; Renewing Computing Paradigms for More Efficient Parallelization of Single-Threads; MongoDB as an Efficient Graph Database: An Application of Document Oriented NOSQL Database; Big Data Analytics for Prevention and Control of HIV/AIDS; Performance Analysis of Deadlock Prevention and MUTEX Detection Algorithms in Distributed Environment.…”
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Hands-On Data Warehousing with Azure Data Factory : ETL techniques to load and transform data from various sources, both on-premises and on cloud. by Kamrat Gutzait, Michelle

Table of Contents: “…Ways to directly copy files into the Data LakePrerequisites for the next steps; Creating a Data Lake Analytics resource; Using the data factory to manipulate data in the Data Lake; Task 1 -- copy/import data from SQL Server to a blob storage file using data factory; Task 2 -- run a U-SQL task from the data factory pipeline to summarize data; Service principal authentication; Run U-SQL from a job in the Data Lake Analytics; Summary; Chapter 5: Machine Learning on the Cloud; Machine learning overview; Machine learning algorithms; Supervised learning; Unsupervised learning; Reinforcement learning.…”
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Big data, data mining and machine learning : value creation for business leaders and practitioners by Dean, Jared, 1978-

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Boosting : foundations and algorithms by Schapire, Robert E.

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Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA

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Computational Intelligence, Evolutionary Computing, Evolutionary Clustering Algorithms. by Kristensen, Terje

Table of Contents: “…Cluster Validation; Evolutionary Algorithms ; 3.1. INTRODUCTION; 3.1.1. Data Representation Chromosome; 3.1.2. …”
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Molecular bioinformatics : algorithms and applications by Schulze-Kremer, S. (Steffen)

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Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms. by Ozdemir, Caner

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