In this section, we discuss the centroid-based clustering technique and its computational challenges. An example of using K-means with Spark MLlib will be shown for a better understanding of the centroid-based clustering.
- Preface
- Introduction to Scala
- Object-Oriented Scala
- Functional Programming Concepts
- Collection APIs
- Tackle Big Data – Spark Comes to the Party
- Start Working with Spark – REPL and RDDs
- Special RDD Operations
- Introduce a Little Structure - Spark SQL
- Stream Me Up, Scotty - Spark Streaming
- Everything is Connected - GraphX
- Learning Machine Learning - Spark MLlib and Spark ML
- Introduction to machine learning
- Spark machine learning APIs
- Feature extraction and transformation
- Creating a simple pipeline
- Unsupervised machine learning
- Binary and multiclass classification
- Summary
- My Name is Bayes, Naive Bayes
- Time to Put Some Order - Cluster Your Data with Spark MLlib
- Text Analytics Using Spark ML
- Spark Tuning
- Time to Go to ClusterLand - Deploying Spark on a Cluster
- Spark architecture in a cluster
- Deploying the Spark application on a cluster
- Submitting Spark jobs
- Hadoop YARN
- Configuring a single-node YARN cluster
- Step 1: Downloading Apache Hadoop
- Step 2: Setting the JAVA_HOME
- Step 3: Creating users and groups
- Step 4: Creating data and log directories
- Step 5: Configuring core-site.xml
- Step 6: Configuring hdfs-site.xml
- Step 7: Configuring mapred-site.xml
- Step 8: Configuring yarn-site.xml
- Step 9: Setting Java heap space
- Step 10: Formatting HDFS
- Step 11: Starting the HDFS
- Step 12: Starting YARN
- Step 13: Verifying on the web UI
- Submitting Spark jobs on YARN cluster
- Advance job submissions in a YARN cluster
- Configuring a single-node YARN cluster
- Apache Mesos
- Deploying on AWS
- Summary
- Testing and Debugging Spark
- PySpark and SparkR
Centroid-based clustering (CC)
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