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

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INTRODUCTION

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The cloud computing (CC) plays a major role to addressboth usages of data storage and computational of hugedata, especially for mining and knowledge discoveryapplications.

But, also it requires dealing with process of datain efficient and cost-effective manner (as low).

The data mining (DM) concept is used for extractinguseful data or patterns from huge database librarieslike data marts, databases, extensible mark-uplanguage (XML) data, data warehouses, files etc.

Additionally, with regards to DM, primary stage is ofKnowledge Discovery in Databases (KDD).


DM METHOD

The DM methods include clustering, classificationand association rule mining (ARM).

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

The classification method is a process of developing aunique model with regards to the classes which utilizedata features.

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

Especially, ARM used for extracting the interestingrelationships, general structures, correlations, frequentpatterns 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 indata is termed as Knowledge discovery in databases (KDD)

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THE GENERAL PROCESS FLOWFOR KNOWLEDGE DISCOVERY

PROCESS


Selection

Data

Pre-processing

Target Data

Transformation

Pre-processedData

Data Mining

TransformedData

Evaluation /Interpretation

Patterns

Knowledge


GENERAL PROCESS FLOWThe general process flow of the knowledge discovery process is discussed as follows:

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SelectionThis step focuses on a subset of data samples orvariables or creating target dataset, on which discoveryare needs to be performed.

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

Pre-processing

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TransformationThe data transformation is used for datatransformation or dimensionality reduction methods.

Data MiningIn a particular representational form, this stages used on the searchingfor patterns of interest-based on the DM specific goals for instancepredication.

Interpretation/EvaluationThis stage involves the evaluation and interpretationof mined patterns.

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RECOMMENDATION

The user can minimize the execution time of DM methods through concurrentlyexploiting both scalable properties of CC and parallel programming approach.This might be use KDP with reasonable cost and accessible from anywhere thatcreates data management and storage easier.

To deploy the secure cloud framework based on a private, public network thatprovides 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, predictionand learning process in a cloud database. This will enhance the systemperformance in terms of accuracy and execution time.

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