Data Mining Laboratory

The Objectives of Data Mining Lab is

•  To understand the need of Data Warehouses over Databases, and the difference between usage of operational and historical data repositories.

•  To be able to differentiate between RDBMS schemas & Data Warehouse Schemas.

•  To understand the concept of Analytical Processing (OLAP) and its similarities & differences with respect to Transaction Processing (OLTP).

•  To conceptualize the architecture of a Data Warehouse and the need for pre-processing.

•  To understand the need for Data Mining and advantages to the business and scientific world. The validating criteria for an outcome to be categorized as Data Mining result will be understood.

•  To get a clear idea of various classes of Data Mining techniques, their need, scenarios (situations) and scope of their applicability.

•  To learn the algorithms used for various types of Data Mining Problems.

List of Software Available:

Open Source:

•  ADaM, Algorithm Development and Mining version 4.0 toolkit

•  Alpha Miner, open source data mining platform that offers various data mining model building and data cleansing functionality.

•  CRAN Task View: Machine Learning & Statistical Learning, machine learning and statistical packages in R.

•  Data bionic ESOM Tools, a suite of programs for clustering, visualization, and classification with Emergent Self-Organizing Maps (ESOM).

•  Gnome Data Mining Tools, including apriori, decision trees, and Bayes classifiers.

•  KNIME, extensible open source data mining platform implementing the data pipelining paradigm (based on eclipse).

•  Machine Learning in Java (MLJ), an open-source suite of Java tools for research in machine learning.

•  MLC++, a machine learning library in C++. Kansas State U. port of MLC++: Binary (tar.gz), and Linux source

•  Rapid Miner, a leading open-source system for knowledge discovery and data mining.

• TANAGRA, offers a GUI interface and methods for data access, statistics, feature selection, classification, clustering, visualization, association and more.

•  Weka, collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform.

Commercial

  •  IBM® SPSS® Modeler, It is a powerful, versatile data mining workbench that helps you build accurate predictive models quickly and intuitively, without programming.

•  Matlab (Data Analysis Toolbox).