Utilizing Data Mining and Machine Learning for Business Intelligence

Enterprise Business Intelligence
Abstract
This activity is intended to give the great chance to utilize the data mining and
machine learning in strategy in discovery knowledge from a dataset and investigate the

applications for business knowledge. This task investigation the health news dataset to
investigate the weka data mining applications. This task partitioned in to 10 reasonable. In
First, we will introduce the weka programming and download the Data archive. In second,
this reasonable is utilized to do data and data pre-preparing for provided dataset. In third, this
practical is utilized for data visualization and dimension reduction. In fourth, this useful is
utilized to do the clustering algorithm like K-Means. In Fifth, this task is utilized to
supervised mining that is classification algorithm on weka. In sixth down to earth, this useful
is utilized to do execution assessment on weka experimenter and Knowledge Flow. In
seventh, this useful is utilized for anticipating the time series on weka packet manager. In
eighth, this assignment is utilized to do text mining. In final, this task is utilized to do the
image analysis on weka. These are will be analysed and discussed in detail.
Table of Contents
1 Introduction......................................................................................................................3
2 Data set..............................................................................................................................3
1

3 Data mining Techniques..................................................................................................3
4 Evaluation and Demonstration.......................................................................................4
4.1 Practical – 1................................................................................................................4
4.2 Practical – 2................................................................................................................6
4.3 Practical – 3................................................................................................................9
4.3.1 Visualising the Dataset.......................................................................................9
4.3.2 Visualising the Dataset using Classifiers........................................................12
4.4 Practical – 4..............................................................................................................18
4.4.1 Manually Working with K-Means..................................................................18
4.4.2 Unsupervised Learning in WEKA – Clustering............................................20
4.5 Practical – 5..............................................................................................................22
4.6 Practical – 6..............................................................................................................29
4.6.1 Weka Experimenter.........................................................................................29
4.6.2 Weka Knowledge Flow....................................................................................33
4.7 Practical – 7..............................................................................................................39
4.8 Practical – 8..............................................................................................................46
4.8.1 Training the Classifier Model.........................................................................46
4.8.2 Predict the Class in Test..................................................................................48
4.9 Practical – 9..............................................................................................................50
5 Conclusion.......................................................................................................................55
References...............................................................................................................................56
1 Introduction
2

This activity is intended to give the great chance to utilize the data mining and machine
learning in strategy in discovery knowledge from a dataset and investigate the applications
for business knowledge. This task investigation the health news dataset to investigate the
weka data mining applications. This task partitioned in to 10 reasonable. In First, we will
introduce the weka programming and download the Data archive. In second, this reasonable
is utilized to do data and data pre-preparing for provided dataset. In third, this practical is
utilized for data visualization and dimension reduction. In fourth, this useful is utilized to do
the clustering algorithm like K-Means. This task separated into two parts such as Part 1 and
part 2. The part 1 is manually calculated the K-means for provided data set. The part 2 is to
use weka clustering algorithm to calculate the K-Means. In Fifth, this task is utilized to
supervise data mining that is classification algorithm on weka. In sixth down to earth, this
useful is utilized to do execution assessment on weka experimenter and Knowledge Flow. In
seventh, this useful is utilized for anticipating the time series on weka packet manager. In
eighth, this assignment is utilized to do text mining. In final, this task is utilized to do the
image analysis on weka. These are will be analysed and discussed in detail.
2 Data set
Each record is related to one Twitter record of a news office. For example, bbchealth.txt
is related to BBC prosperity news. Each line contains tweet id | date and time | tweet. The
separator is '|'. This substance data has been used to evaluate the execution of point models on
short substance data. In any case, it might be used for various assignments, for instance,
clustering ("Classification and clustering", 2018).
3 Data mining Techniques
Data mining techniques is demonstrated as underneath.
3

4 Evaluation and Demonstration
4.1 Practical – 1
This task, we will introduce the weka programming and download the Data set. To begin
with, client needs to download and introduce the weka programming. When weka introduced
effectively, client requires the open the weka programming. It is represented as beneath
("Cluster Analysis", 2018).
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After, download the UCI data repository by using the below link.
https://archive.ics.uci.edu/ml/datasets/Health+News+in+Twitter
After, click the data folder
Then, click the Health_News_Tweets.zip file to download the data set. The download data set
is attached below.
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4.2 Practical – 2
This task is utilized to do data and data pre-preparing for given data collection. To do the data
pre-processing on Weka by takes after the underneath steps. To start with, client needs to
open the weka ("Data Mining - Weka | Hitachi Vantara Community", 2018).
After, click the Explorer to load the data set.
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Data pre-processing process is completed.
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