Data Mining Report: Analysis of Industry Directions in Data Mining

This report provides an overview of data mining, focusing on its application in identifying industry directions. It begins by outlining the context of data mining and explores various data sources, including a student database named 'MCSDB'. The report then delves into different data mining tools and techniques, such as RapidMiner, Orange, Weka, KNIME, and KEEL, highlighting their strengths and weaknesses. It also discusses the classification of data and provides findings from the analysis, including cluster analysis, regression analysis, and classification analysis. Furthermore, the report examines potential uses of data mining, such as market segmentation, customer churn prediction, fraud detection, and direct/interactive marketing. Finally, the report addresses relevant legislation, including the Privacy Act 1988, Copyright Act 1968, and Freedom Act 1982, ensuring ethical and legal considerations in data mining activities. The report concludes by summarizing the potential uses of data mining and offering recommendations for its application.

Mine Data to Identify
Industry Directions

 Data mining context
 Data sources
 Explain the tools and techniques
 Explain classification of data
 Findings of analysis
 Draw insights
Table of content

Uses of Data Mining Explanation
Cluster analysis This gives an opportunity for identifying a single user target group in
accordance to similar features within database like education level,
age, class, course, etc.
Regression analysis This helps in enabling to study habits, changes, customer satisfaction
level for making marketing forecasts. This will help in analysing the
market effectively.
Classification analysis This helps in allowing to classify various information that is received
from potential clients and customers while identifying patterns within
a database.
Intrusion detection System security is the most important concept of an organisation so
data mining is helpful in protecting data from various intruders.
Fraud Detection This will be used in detecting any fraud within the organisation.
There is requirement of perfect fraud detection system so that data
available for customers is authentic and valid.

Data sources
There is a student database in MC that is named as 'MCSDB'. This
contains data of each and every student. DigiGeek is given all
snapshots of student's data. This project needs external data for
ensuring precision in recommendation to the organisation. It is
responsibility of market analyst for identifying and obtaining
data of international students and overall enrolment level.

Tools and Techniques
RapidMiner – This is known as best predictive analysis system that is developed by a company
naming Rapid Miner. This is written in Java programming.
Strength Weakness
 This gives an integrated environment for text
mining, deep learning, predictive analysis and
machine learning.
 This tool is useful for wide range of business
application and education.
 This offers server on premise as well as public
and private cloud infrastructure.
 Coding is not used in this application.
 For using this tool, expensive and commercial
tool is required.
 Use case of this application is limited to set of
modules and processors.

Cont....
Orange – This is a software suite for data mining and this helps in data visualisation and this is a
component based software. This is written in python language.
Strength Weakness
 Orange application helps in bringing more
interactive vibe in analytical tool.
 Data that is coming to Orange is quickly
transformed to requisite pattern.
 This application helps users to make smart
decisions in less time.
 The installation of this application is big as there is
need of installing QT.
 It contains limited list of learning algorithms.
 This application is limited to exporting
representing data visually.

Cont....
Weka – Waiketo Environment is used for analysis of knowledge. This is a collection of algorithm of machine learning
used for data mining activities. This is supported by Java and this consists of collection of different tools for data
analytics.
Strength Weakness
 This is suitable for making schemes for machine
learning.
 It is open source , extensible and free and it can be
integrated to other Java packages.
 This is a secure way of using data.
 This application lacks adequate and proper
documentation.
 It has bad connectivity with Excel spreadsheet and
database with non Java programs.
 This application is weaker in classical statistics.

Cont...
KNIME – Konkstanz Information Miner is an open source data analytics, integration and reporting platform which is
used in research. This is generally used in different fields like business intelligence, CRM customer data analytics
and financial data analysis.
Strength Weakness
 It integrates different analysis modules.
 This is an easy data mining tool which offers
access to different statistical routines.
 This has ability for interface with programs
which allows for visualisation and analysis of
data.
 Limited error measurement method.
 There is no wrapper method for descriptor
selection.
 There is no facility for parameter optimisation.

Cont....
KEEL – This is Knowledge extraction based on Evolutionary which is an application of machine learning software
tools. This is used for data manipulation in research.
Strength Weakness
 This consists of classification, regression, pattern mining
and clustering.
 This includes huge collection of preprocessing
techniques and knowledge extraction algorithms.
 The main limitation of this is that efficiency is restricted
by a number of algorithms.
 This is less used in organisations.

Type of legislations
The three type of legislations that are currently in force are listed below-
This policy is related to communicate with staff of DigiGeek. This helps in managing,
collecting, dealing with and allowing access to personal data according to Privacy
Act 1988 and Australian Privacy Principles.
Data mining activities are conducted without violating copyright in alignment with
Copyright Act 1968.
Data and information are accessed by Australian Government agencies according to
Freedom Act 1982.

Potential uses of data mining
The potential uses of data mining and recommendations are mentioned below -
Market segmentation – This includes identifying the similar characteristics of individuals
working in the organisation.
Customer churn - This predicts which customers are going to leave the organisation.
Fraud Detection - Determines transactions that are fraudulent.
Direct Marketing – This determines those prospects which are included in a mailing list.
Interactive marketing - This predicts what an individual access at website.