Tips and Concepts of the Knowledge Discovery Process in Cloud Computing- TutorsIndia.com

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Tips & Concepts of the knowledge discovery process in cloud computing Best UK Dissertation Research Topics from Existing Recent Research Gaps in Computer Science Engineering Domain Tags: TutorsIndia | Computer Science Dissertation Topics | Topics in Computer Science | Computer Science Dissertation Writing Services | Task Scheduling Research Dissertation | Dissertation | Assignment | Essay | Statistical Analysis | Coding & Algorithm | Resit Dissertation | Powerpoint | Poster

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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, files etc.

4

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 02

The classification method is a process of developing a unique model with regards to the classes which utilize data features.

03 04

Especially, ARM used for extracting the interesting relationships, general structures, correlations, frequent patterns amongst group of items during transactions .

The clustering approach is utilized toward discover structure and identify groups in unlabelled data.

05

The ARM process is used to discover rules amongst different items in large datasets.

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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

Target Data

Pre-processed Data

Data Mining

Transformed Data

Evaluation / Interpretation Patterns

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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

02

In this stage, comprises the data pre-processing, data cleaning toward obtaining reliable information.

Transformation The data transformation is used for data transformation or dimensionality reduction methods.

03 Data Mining

04 Interpretation/Evaluation This stage involves the evaluation and interpretation of mined patterns.

In a particular representational form, this stages used on the searching for patterns of interest-based on the DM specific goals for instance predication.

05

Tutors India develop ML algorithms using Python, Hadoop framework and many more to booth secure access control in cloud computing platforms. Hiring our experts, you are assured with quality and on-time delivery.

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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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