Data Mining: Definition, Applications, Techniques, and Challenges

Running head: DATA MINING
ASSIGNMENT 1.1- MIT4204-DATA MINING
Name of the Student
Name of the University
Author Note

1DATA MINING
1. What is data mining?
Data is referred to the process of determining the large data sets that involves
interaction of statistics, database systems and machine learning. Data mining includes
analysis of data and summarizing the data for an effective strategy. This is referred to
the process through which the data are extracted from data mines. Data mining is
referred to the method in which the interesting knowledge’s are extracted in the form
of huge data.
a) However data mining is not another hype. Moreover the demand of data has
increased with the wide availability of data sets and the need of converting this
data into useful knowledge. Thus it can be stated that data mining is a result of
information technology.
b) Data mining is more than a simple transformation of technology that converts
databases, statistics and machine learning into useful data. Data mining includes
integrating the data instead of simply transforming the data. This includes image
and signal processing, analysing spatial data, neural networks, recognition of
patterns, high performance computing and statistics analysis.
c) The development of database technology took place with the development in the
creation of database and data collection method. This led to development of
efficient mechanism that helps in data management. The data management
process includes retrieval and data storage, query and transaction processing.
Several database systems offers transaction and query processing that led to better
data analysis. Thus it can be stated that data mining started its development in
order to meet the requirement.
d) The steps involved in data mining includes a process that will discover the
knowledge. The steps are as follows:

2DATA MINING
 Data cleaning: this is the process that includes removal of unwanted data
and transforms the noise.
 Data integration: this stage includes combining multiple data sources in
order to obtain a proper data set.
 Data selection: this stage includes selecting the data relevant to analysis of
data after retrieving it from the database.
 Data transformation: this stage is related to transferring the data with the
use of appropriate data mining.
 Data mining: this is an essential process that includes efficiently applying
the methods for extracting the data.
 Pattern evaluation: in this stage the patterns related to the data are being
identified based on some measures.
 Knowledge presentation: this includes visualizing the techniques and
representing the knowledge’s that are used to mine the knowledge.
2. A) Data mining plays a major role in development of the business. For example the
business aligned with selling items and providing services uses data mining for
obtaining benefits in the market. This type of business requires both customer
profiling and cross market analysis. The knowledge based on this can be gathered
with the help of data query processing. However it requires some manual working
from the expert market analysts. This both will help in understand the queries that will
help in managing huge amount of data.
B) There are several data mining architecture that are offered by them. This helps in
developing the application effectively. The data mining architecture that will be
beneficial for this application requires to have some necessary components, this
are as follows:

3DATA MINING
A database warehouse, a database that will contain the set of databases and
spreadsheets that will store the information regarding the student and course.
A database warehouse server that will help in fetching the relevant data from the
system based on the request processed by user at the time of data mining.
Apart from this a knowledge base will help in holding the records related to domain
knowledge that will be used as a guide for searching the interestingness patterns.
Data mining engine will contain a set of functional module for performing certain
tasks. This task includes classification, cluster analysis and evolution.
Pattern evaluation module helps in working with tandem that allows data mining
modules and focuses on searching the interesting patterns. With the use of graphical
user interface will provide user with an effective interactive approach.
c) The main difference between data warehouse and a database are, database is
referred to the collection of interrelated data. This data helps in representing the
current status. Different database tends to have different schema. Apart from this ad
hoc query is supported by database system and also allows on line transaction
processing. Whereas data warehouse is referred to the respiratory of information that
collects multiple resources and stores this data under a unified schema. This also helps
decision support and data analysis process. Apart from this the similarity between
data warehouse and a database is that both contains valuable information in a form of
repositories. Both facilitates the user with the ability of storing persistent data.
d) Object oriented database: this is designed based on OOPP. This paradigm stores data
in form of classes in the form of class hierarchy. The data stored within the database
is referred to as object.