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Data Mining - Practical Machine Learning Tools and Techniques

   

Added on  2023-04-23

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Running head: DATA MINING
DATA MINING – PRACTICAL MACHINE LEARNING TOOLS
AND TECHNIQUES
Name of the Student
Student ID
Name of the University
AUTHOR Name
(Ian H. Written, Eibe Frank, Mark A. Hall & Christopher J. Pal)
Data Mining - Practical Machine Learning Tools and Techniques_1

DATA MINING
Table of Contents
Introduction..............................................................................................................................................2
About data mining...................................................................................................................................2
Applications of data mining.....................................................................................................................3
Conclusion...............................................................................................................................................4
References................................................................................................................................................5
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[Name of the Student]
[Student ID]
Data Mining - Practical Machine Learning Tools and Techniques_2

DATA MINING
Introduction
The aim of the paper is to research about data mining. The report is going to discuss
the concepts applied by the researcher in their paper. The report will describe all the
components related to data mining. Data mining is the process through which data are stored
electronically. Data mining basically deals with problem solving and analysing the data that
are already present within the database (Maione ET AL., 2016). The process followed by data
mining needs to be discovered and must be meaningful so that advantages are achieved by
the system. Data mining is referred to interdisciplinary that aims at extracting information
from the computer.
About data mining
Data mining term was appeared in the year 1990. Data mining is referred to analysis
of steeps associated with knowledge discovery related to databases. Apart from this data
mining also includes aspects related to database management, model and interference
considerations. The major difference between data mining and data analysis is to aggregate
the activities that includes analysing effectiveness related to market campaign. The aim of
data mining is too extract knowledge and patterns from large data sets (Witten et al., 2016).
This are also used in information processing that ensures collection, analysis, extraction and
statistics of a data. Data mining activity includes analysis of large set of data automatically.
Analysis of data records are known as cluster analysis, the analysis of unusual records known
as anomaly detection and finally dependencies that re associated with rule mining and
sequential pattern mining. Earlier data mining was done with the help of regression analysis
(Larose, 2015). With the increase in size of data sets the complexity also increases thus it
becomes important to have a cluster analysis, generic algorithms, decision trees and support
vector machine. Data mining is referred to the process that applies this methods in order to
uncover the hidden patterns in case of large data sets. With the help of data mining the gap
between artificial intelligence and applied statistics can be reduced. The process related to
data mining are divided in to different stages that includes the phases: business
understanding, data understanding, data preparation, modeling, evaluation and deployment.
The six data mining classes includes anomaly detection, association rule learning,
clustering, classification, regression and summarization. Anomaly detection includes
identification of unusual data records. The anomalies or data errors that are needed to be
investigated further. Association rule learning searches for the relationship within the
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[Name of the Student]
[Student ID]
Data Mining - Practical Machine Learning Tools and Techniques_3

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