Data Mining

January 21, 2019

An Article written by Trainee Ziyad AlMansouri From King Saud University about Data Mining.

 

 

INTRODUCTION:

 

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

The goal is to extract a knowledge from a large amount of data in a way that makes it easy to understand and make a best use of it.

 

Data set: list of records each of which has values for some predefined fields.

 

 

DATAMINING APPLICATION

 

  • Financial: Fraud Detection, Loan Evaluation, and Credit Risk Analysis.

  • Marketing: Cross-Selling, Campaign Response, and Consumers Response.

  • Biological Data Analysis: Semantic integration of heterogeneous, distributed genomic and proteomic databases and Alignment, indexing, similarity search, and comparative analysis.

  • Intrusion Detection: Association and correlation analysis and Analysis of Stream data.

 

 

1.RapidMiner

2.OracleDatamining

3.IBM SPSS Modeler

4.KNIME

5.Python

6.Orange

7.Kaggle

8.Rattle

9.Weka

10.Teradata 

 

 

 

 

 

References:

  • https://www.analyticsinsight.net/

  • https://searchsqlserver.techtarget.com/definition/data-mining

  • https://www.tutorialspoint.com/data_mining

 

 

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