Tips & Concepts of the knowledge discovery process in cloud computing
                     Research paper
Tips & Concepts of the  
knowledge discovery  
process in cloud 
cBesot UmK Dipsseurtattiion Rgesearch Topics from Existing Recent Research 
Gaps  in Computer Science Engineering Domain
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INTRODUCTION
1 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.
2
But, also it requires dealing with process of data 
 in efficient and cost-effective manner (as low).
3 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, 
file4s etc.
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 
01
 and association rule mining (ARM).
The clustering approach is utilized toward discover 
02
 structure and identify groups in unlabelled data.
The classification method is a process of developing a 
 unique model with regards to the classes which utilize 03
 data features.
The ARM process is used to discover rules amongst 
04  different items in large datasets.
Especially, ARM used for extracting the interesting  Hire Tutors India experts to develop your 
relationships, general structures, correlations, frequent 05  algorithm and coding implementation 
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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 Transformed 
Target Data  Data  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  01
variables or creating target dataset, on which discovery 
 are needs to be performed. Pre-processing
02 In this stage, comprises the data pre-processing, data cleaning 
 toward obtaining reliable information.
Transformation
The data transformation is used for data  03
transformation or dimensionality reduction methods.
Data Mining
In a particular representational form, this stages used on the searching
04 for patterns of interest-based on the DM specific goals for instance 
 predication.
Interpretation/Evaluation
This stage involves the evaluation and interpretation 05
 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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