Knowledge Engineering: Rapid Miner

University
Semester
– KNOWLEDGE ENGINEERING –
RAPID MINOR
Student ID
Student Name
Submission Date
1

Table of Contents
Introduction................................................................................................................................3
Tweet Data Description..............................................................................................................3
Knowledge Creation Techniques...............................................................................................4
1. Classification – Decision Tree........................................................................................4
2. Clustering – SOM (Self Organizing Map)......................................................................7
3. Association Rule Mining – FP Growth Algorithm.......................................................11
External Source Knowledge Creations....................................................................................16
Conclusion................................................................................................................................18
References................................................................................................................................20
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Introduction
The intention of this project the intention of this project is to use the Rapid Miner tool
and evaluate Twitter data for analysis and evaluation of the users who are rapidly increasing
in the online of world of Twitter. With today's modern world of social media and the vast
world of internet data information which is available online platforms like Facebook,
Snapchat, YouTube, WhatsApp, Twitter etc. We shall be monitoring and evaluating activities
of the users who are regularly using and posting on Twitter. Using Rapid miner, data science
software platform we shall analyse, collect, and evaluate this user data. More and more
people are expressing their experience information feelings news messages wishes etc social
media platforms commonly Facebook flicker YouTube Snapchat Twitter and WhatsApp. By
using devices, applications, tools, gadgets, websites hand online media this activities are
carried out. With online information, news, social interaction, images, videos, links and
messages, Twitter has become one of the most popular and widely used social media
platforms. Tweets which are posted by the users and account holders on Twitter are stored,
saved and sometimes even retweeted. Twitter saves all this data hand information of all the
social media post on its server. With all the real-time updates, posts, images, links, Twitter
has become very dynamic data streaming service and system. It is interactive and user-
friendly social media platform. We shall now apply methodology and procedure to evaluate
all this vast data hand the users information respect to the tweets on Twitter so that we can
understand and try to evaluate the activities of the users on Twitter. Knowledge engineer
shall carry out and in depth survey and procure all the different data packets, information sets
and apply on it so we can understand what the user wants and his / her activities on the social
media platform, Twitter (Challenges with Big Data Analytics, 2015).
Tweet Data Description
So, in this project we shall be using the data sets and packets that the users and
account holders of Twitter post on this social media platform. Analysis will be done by
gathering all these information from the Twitter server. By this generated data of the users
from Twitter, we shall apply some few selected attributes and study the outcome to
understand the online characteristics and activities of these users. The following are the
attribute’s which shall be used for this project study,
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 ID
 Time
 Content
 UserId
 UserHomeTown
 Location
Knowledge Creation Techniques
For the process and study of the Twitter account holders and users, we shall select a few
methodologies and use these as knowledge creating techniques on this data (C and Babu,
2016). For this procedure, we have selected the below given three “knowledge creation
techniques”,
1. Classification as decision tree
2. Clustering as Self Organizing Map (SOM)
3. Association rule mining as FP growth by using the Rapid Miner tool
We shall study each of this methodology by discussing one by one in detail.
1. Classification – Decision Tree
For the collected data of the tweets (from Twitter), the “Decision Tree” is represented as
below,
Once we have created the “Decision Tree”, we shall use the Rapid Miner tool on the
collected twitter data for analysis.
a) Select Attributes
b) Decision Tree
c) Performance
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