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Data Mining & BI Lab Guide

This document outlines 10 experiments for a course on data mining and business intelligence. The experiments include demonstrations of preprocessing data, association rule mining using apriori algorithm, classification using decision trees with j48 and id3 algorithms, clustering with k-means, and a mini business intelligence project applying data mining techniques to a case study dataset. Students will also learn HDFS commands and how to use data mining tools like WEKA, RapidMiner, and XLMiner.

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Rancho Chauhan
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57 views2 pages

Data Mining & BI Lab Guide

This document outlines 10 experiments for a course on data mining and business intelligence. The experiments include demonstrations of preprocessing data, association rule mining using apriori algorithm, classification using decision trees with j48 and id3 algorithms, clustering with k-means, and a mini business intelligence project applying data mining techniques to a case study dataset. Students will also learn HDFS commands and how to use data mining tools like WEKA, RapidMiner, and XLMiner.

Uploaded by

Rancho Chauhan
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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DATA MINING

&
BUSINESS INTELLIGENCE
(2170715)
Index
Sr. No. Experiment Page No. Signature
1. Introduction to various Data Mining Tools
(WEKA, RAPID MINER, XLMINER). Their
differences, Pros & Cons & Liitations.
2. Demonstration of preprocessing on dataset
student.arff and labor.arff
3 Demonstration of Association rule process on
dataset using apriori algorithm
4 Demonstration of classification rule process on
dataset using j48 algorithm
5 Demonstration of classification rule process on
dataset using id3 algorithm
6 Demonstration of classification rule process on
dataset using naïve bayes algorithm
7 Demonstration of clustering rule process on
dataset using simple k-means
8 Demonstration of decision tree
9 Business Intelligence Mini Project: Each group
assigned one new case study for this; A BI report
must be prepared outlining the following steps:
a) Problem definition, Identifying which data
mining task is needed b) Identify and use a
standard data mining dataset available for the
problem. Some links for data mining datasets
are: WEKA site, UCI Machine Learning
Repository, KDD site, KDD Cup etc. c)
Implement the data mining algorithm of choice
d) Interpret and visualize the results e) Provide
clearly the BI decision that is to be taken as a
result of mining.
10 Introduction to HDFS Commands

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