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Data Mining And Data Warehousing

by AmindianAmindian
Type: PracticalCourse: B.Tech Specialization: Computer Science EngineeringOffline Downloads: 8Views: 229Uploaded: 4 months ago

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Data Mining Lab LABORATORY MANUAL on DATA MINING Prepared by INDRANEEL K Associate Professor CSE Department DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SRI KOTTAM TULASI REDDY MEMORIAL COLLEGE OF ENGINEERING (Affiliated to JNTU, Hyderabad, Approved by AICTE, Accredited by NBA) KONDAIR, MAHABOOBNAGAR (Dist), AP - 509125 S.K.T.R.M College of Engineering 1
Data Mining Lab INDEX The objective of the lab exercises is to use data mining techniques to identify customer segments and understand their buying behavior and to use standard databases available to understand DM processes using WEKA (or any other DM tool) 1. Gain insight for running pre- defined decision trees and explore results using MS OLAP Analytics. 2. Using IBM OLAP Miner – Understand the use of data mining for evaluating the content of multidimensional cubes. 3. Using Teradata Warehouse Miner – Create mining models that are executed in SQL. ( BI Portal Lab: The objective of the lab exercises is to integrate pre-built reports into a portal application ) 4. Publish cognos cubes to a business intelligence portal. Metadata & ETL Lab: The objective of the lab exercises is to implement metadata import agents to pull metadata from leading business intelligence tools and populate a metadata repository. To understand ETL processes 5. Import metadata from specific business intelligence tools and populate a meta data repository. 6. Publish metadata stored in the repository. 7. Load data from heterogeneous sources including text files into a predefined warehouse schema S.K.T.R.M College of Engineering 2
Data Mining Lab CONTENTS S.no Experiment Week NO Page NOs 1 Defining Weather relation for different attributes 1 7-18 2 Defining employee relation for different attributes 2 19-28 3 Defining labor relation for different attributes 3 29-38 4 Defining student relation for different attributes 4 39-49 5 Exploring weather relation using experimenter and obtaining results in various schemes Exploring employee relation using experimenter 5 49-59 6 60-65 7 Exploring labor relation using experimenter 7 66-71 8 Exploring student relation using experimenter 8 72-78 9 Setting up a flow to load an arff file (batch mode) andperform a cross validation using J48 Design a knowledge flow layout, to load attribute selection normalize the attributes and to store the result in a csv saver. 9 86-112 10 116-117 6 10 S.K.T.R.M College of Engineering 3
Data Mining Lab Aim: Implementation of Data Mining Algorithms by Attribute Relation File formats Introduction to Weka (Data Mining Tool) • Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset (using GUI) or called from your own Java code (using Weka Java library). • Tools (or functions) in Weka include: • Data preprocessing (e.g., Data Filters), • Classification (e.g., BayesNet, KNN, C4.5 Decision Tree, Neural Networks, SVM), • Regression (e.g., Linear Regression, Isotonic Regression, SVM for Regression), • Clustering (e.g., Simple K-means, Expectation Maximization (EM)), • Association rules (e.g., Apriori Algorithm, Predictive Accuracy, Confirmation Guided), • Feature Selection (e.g., Cfs Subset Evaluation, Information Gain, Chisquared Statistic), and • Visualization (e.g., View different two-dimensional plots of the data). Launching WEKA The Weka GUI Chooser (class weka.gui.GUIChooser) provides a starting point for launching Weka‘s main GUI applications and supporting tools. If one prefers a MDI (―multiple document interface‖) appearance, then this is provided by an alternative launcher called ―Main‖ (class weka.gui.Main). The GUI Chooser consists of four buttons one for each of the four major Weka applications and four menus. The buttons can be used to start the following applications: • Explorer An environment for exploring data with WEKA (the rest of this documentation deals with this application in more detail). • Experimenter An environment for performing experiments and conducting statistical tests between learning schemes. • Knowledge Flow This environment supports essentially the same functions as the Explorer but with a drag-and-drop interface. One advantage is that it supports incremental learning. • Simple CLI Provides a simple command-line interface that allows direct execution of WEKA commands for operating systems that do not provide their own command line interface. S.K.T.R.M College of Engineering 4

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