Recent Master’s Research Topic Ideas for Electrical and Electronics Engineering 2020 (1)

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Tips Concepts of the knowledge discovery
process in cloud computing
Research paper
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INTRODUCTION
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The cloud computing (CC) plays a major role to
address both usages of data storage and
computational of huge data, especially for
mining and knowledge discovery applications.
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But, also it requires dealing with process of
data in efficient and cost-effective manner (as
low).
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The data mining (DM) concept is used for
extracting useful data or patterns from huge
database libraries like data marts, databases,
extensible mark-up language (XML) data, data
warehouses, files etc.
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Additionally, with regards to DM, primary stage
is of Knowledge Discovery in Databases (KDD).
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DM METHOD
The DM methods include clustering, classification
and association rule mining (ARM).
01
The clustering approach is utilized toward
discover structure and identify groups in
unlabelled data.
02
The classification method is a process of
developing a unique model with regards to the
classes which utilize data features.
03
The ARM process is used to discover rules amongst
different items in large datasets.
04
Especially, ARM used for extracting the
interesting relationships, general structures,
correlations, frequent patterns amongst group of
items during transactions .
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KNOWLEDGE DISCOVERY IN DATABASES (KDD)
The process of extracting previously unknown data
and reasonable hidden patterns in data is termed
as Knowledge discovery in databases (KDD)
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THE GENERAL PROCESS FLOW FOR KNOWLEDGE DISCOVERY
PROCESS
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Selection Data
Pre-processing
Transformation
Data Mining
Pre-processed Data
Transformed Data
Target Data
Evaluation / Interpretation Patterns
Knowledge
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GENERAL PROCESS FLOW
The general process flow of the knowledge
discovery process is discussed as follows
Selection This step focuses on a subset of data
samples or variables or creating target dataset,
on which discovery are needs to be performed.
01
Pre-processing In this stage, comprises the data
pre-processing, data cleaning toward obtaining
reliable information.
02
Transformation The data transformation is used
for data transformation or dimensionality
reduction methods.
03
Data Mining In a particular representational
form, this stages used on the searching for
patterns of interest-based on the DM specific
goals for instance predication.
04
Interpretation/Evaluation This stage involves the
evaluation and interpretation of mined patterns.
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RECOMMENDATION
The user can minimize the execution time of DM
methods through concurrently exploiting both
scalable properties of CC and parallel
programming approach. This might be use KDP with
reasonable cost and accessible from anywhere that
creates data management and storage easier.
To deploy the secure cloud framework based on a
private, public network that provides
authenticated access to the system by effective
cryptography approach. Also possible to expand
this framework by integrating different DM
techniques.
The deep learning model will be an effective
method for classification, prediction and
learning process in a cloud database. This will
enhance the system performance in terms of
accuracy and execution time.
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