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University of Waikato

Advanced Data Mining with Weka

University of Waikato via FutureLearn

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Overview

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Extend your repertoire of data mining scenarios and techniques

This course will bring you to the wizard level of skill in data mining, following on from Data Mining with Weka and More Data Mining with Weka, by showing how to use popular packages that extend Weka’s functionality. You’ll learn about forecasting time series and mining data streams. You’ll connect up the popular R statistical package and learn how to use its extensive visualisation and preprocessing functions from Weka. You’ll script Weka in Python – all from within the friendly Weka interface. And you’ll learn how to distribute data mining jobs over several computers using Apache SPARK.

This course is aimed at anyone who deals in data. You should have completed Data Mining with Weka and More Data Mining with Weka – or be an experienced Weka user. Although the course includes some scripting with Python, you need no prior knowledge of the language. You will have to install and configure some software components; we provide full instructions.

Before the course starts, download the free Weka software. It runs on any computer, under Windows, Linux, or Mac. It has been downloaded millions of times and is being used all around the world.

(Note: Depending on your computer and system version, you may need admin access to install Weka.)

Syllabus

  • Time series forecasting
    • Introduction
    • How can you use data mining to foretell the future?
    • Time series: linear regression with lags
    • Using the time series forecasting package
    • Looking at forecasts
    • Lag creation and overlay data
    • Analyzing infrared data from soil samples
  • Data stream mining
    • How can you mine continuous data streams?
    • Incremental classifiers in Weka
    • Weka's MOA package
    • The MOA interface
    • Dealing with change
    • Classifying tweets
    • Signal peptide prediction
    • How are you getting on?
  • Reaching out to other data mining packages
    • What’s in Weka's LibSVM and LibLINEAR packages?
    • LibSVM and LibLINEAR
    • How do you access R from Weka?
    • Setting up R with Weka
    • Using R to plot data
    • Using R to run a classifier
    • Using R to preprocess data
    • Analyzing functional MRI Neuroimaging data
  • Distributed processing
    • Can you distribute Weka jobs over several machines?
    • What is Distributed Weka?
    • Distributing Knowledge flows
    • Using Naive Bayes and JRip
    • Map tasks and Reduce tasks
    • Miscellaneous Distributed Weka capabilities
    • Image classification
  • Scripting Weka
    • How can you script Weka?
    • Invoking Python from Weka
    • Building models
    • Visualization
    • Invoking Weka from Python
    • A data mining challenge, and some Groovy
    • Course summary
    • Farewell

Taught by

Ian Witten

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4.6 rating at FutureLearn based on 8 ratings

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