RAPIDMINER: Introduction To Datamining

RAPIDMINER: INTRODUCTION TO DATAMINING

AGENDA
What is Data Mining?
Introduction to RapidMiner
Use of RapidMiner for Data Mining
Download and Installation Steps
Memory Usage , Plug-ins & Settings
Supported File Formats

What is Data Mining?
Process of analyzing data and extracting patterns from it
Four step process:
Classification – Arrange into predefined groups
Clustering – Similar to classification but the groups are not predefined
Regression – To find a function that models the data with least error
Association rule learning – Search for relationships

Different levels of analysis that are available:
Artificial neural networks – Non-linear predictive models that resemble biological neural networks in structure.
Genetic algorithms - Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
Decision trees – Provide a set of rules that you can apply to a new dataset to predict the outcome.
Examples:
Classification and Regression Trees (CART)
Chi Square Automatic Interaction Detection (CHAID) .
CART and CHAID are decision tree techniques used for classification of a dataset.
Rule induction – The extraction of useful if-then rules from data based on statistical significance.
Nearest neighbor – Classify records based on the k-most similar records
Data visualization - Visual interpretation of complex relationships in multidimensional data.

Applications
Can be divided into four major kinds:
Classification
Numerical prediction
Association
Clustering
Some examples:
Automatic abstraction
Financial forecasting
Targeted marketing
Medical diagnosis
Credit card fraud detection
Weather forecasting etc.

Introduction to RapidMiner
RapidMiner (formerly YALE*)is an environment for machine learning and data mining experiments.
RapidMiner is used for both research and real-world data mining tasks.
Software versions:
Community edition (open – source)
Enterprise edition
(Community Edition + More Features + Services + Guarantees)
*YALE - Yet Another Learning Environment

Some properties of RapidMiner:
Written in Java
Knowledge discovery processes are modelled as operator trees
Internal XML representation ensures standardized interchange format of data mining experiments
Scriptinglanguage allows for automating large-scale experiments
Multi-layered data view concept ensures efficient and transparent data handling
GUI, command-line mode (batch mode), and Java API for using RapidMiner from other programs
Several plugins already exist
A large set of high-dimensional visualization schemes for data and models offered by its plotting facility.
Applications: text mining, multimedia mining, feature engineering, data stream mining and tracking drifting concepts, development of ensemble methods, and distributed data mining.

Use of RapidMiner for Data Mining
Using RapidMiner
Process configuration provided as XML file
GUI can be used to design XML description of the operator tree
Break points can be used to check the intermediate results
Use from a separate program
Command line version and Java API can be used to invoke RapidMiner in your programs without using the GUI

Download and Installation Steps
Download
The latest version of RapidMiner can be downloaded from
http://rapid-i.com/content/blogsection/7/82/lang,en/
by selecting the appropriate version(Windows x86, x64 etc.) and RapidMiner edition
Installation
Windows executable
Download the windows executable (.exe) file
Double-click the rapidminer-xxx-instal.exe file to run it
Follow the instructions

JAVA version(any platform)
Install JRE 1.5 or above (http://www.java.sun.com)
Choose the installation directory and unzip the downloaded file (.zip,.tar etc)
rapidminer-xxx-bin.zip – contains only the binaries
rapiminer-xxx-src.zip – contains binaries + source

Memory Usage, Plugins & General Settings
Memory Usage
Complicated Greater memory
Data mining tasks needed
If memory requirements are not met, the entire process might halt.
Windows – amount of memory available is automatically calculated and properly set
Java version – amount of usable memory can be increased during installation.

Plugins
Windows – Executable installer(.exe) to automatically install the plugin to its correct location
Java Version – Copy .jar file to lib/plugins subdirectory
General Settings
Configuration files:
Rapidminerrc
Rapidminerrc.OS (OS : Linux, Windows)
Locations scanned
RapidMiner Home directory
.rapidminer in your home directory
Current working directory
File specified by rapidminer.rcfile
Order

Supported File Formats
Can read data files, read & write models, parameter sets and attribute sets.
Most important – examples and instances

Data files & attribute description files
ARFFEXAMPLESOURCE - .arff format
DATABASEEXAMPLESOURCE – To read from databases
SPARSEFORMATEXAMPLESOURCE
DENSEFORMATEXAMPLESOURCE
Attribute description file (.aml) in order to retrieve metadata about the instances
XML Attributes that can be set:
Name – unique name of the attribute
Sourcefile – name of the file containing the data(default used if not specified)
Sourcecol –column within the file(Starting from 1)
Sourcecol_end – sourcecol-sourcecol_end attributes are generated with the same properties.
Valuetype– one out of nominal,numeric, integer, real, ordered, binominal, polynominal and file_path
Blocktype – one out of single_value, value_series, value_series_start, value_series_end, interval, interval_start, interval_end

Model files (.mod files)
Contains the models generated by previous runs
MODELWRITER – to write model files
MODELLOADER – to read model files
MODELAPPLIER – to apply model files
Attribute construction files (.att files)
ATTRIBUTECONSTRUCTIONWRITER – writes an attribute set
ATTRIBUTECONSTRUCTIONLOADER – reads an attribute set
Parameter set files (.par files)
GRIDPARAMETEROPTIMIZTION – generates a set of optimal parameters for a particular task
PARAMETERSETLOADER – use the parameter files
Attribute weight files (.wgt files)
Attibute selection is seen as attribute weighing which allows for more flexibility
ATTRIBUTEWEIGHTSWRITER – to write attribute weights to a file
ATTRIBUTEWEIGHTSLOADER – to read the attribute weights
ATTRIBUTEWEIGHTSAPPLIER – to apply in the example sets

THANK YOU!

More questions…
Reach us at support@dataminingtools.net
VISIT: WWW.DATAMININGTOOLS.NET

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RAPIDMINER: Introduction To Datamining

by DataminingTools Inc on Mar 02, 2010

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