1. Data Mining :
Data mining is defined as a process used to extract usable data from larger set of any raw data.
Some key features of data mining are –
- Automatic Pattern Prediction based on trend and behavior analysis.
- Predictions based on likely outcomes.
- creation of decision Oriented Information.
- Focus on large data and databases for analysis.
- Clustering based on group of facts not previously known.
2. Online analytical Processing (OLAP) :
OLAP is a computer processing that enables a user to easily and selectively extract and view data from different points of view. It allows user to analyze database information from multiple database systems at one time. OLAP data is stored in multidimensional databases.
Some key features of OlAP are –
- Multidimensional views of data.
- Support for complex calculations.
- Time intelligence.
Some applications of OLAP are –
- OLE DB for OLAP
- Database marketing
- Marketing and sales analysis.
Difference between Data Mining and OLAP :
|1.||Multidimensional Analysis.||Online analytical processing.|
|2.||It has large number of dimensions.||It has limited number of dimensions.|
|3.||Deals with the summary of data.||Deals with the detailed Transaction level data.|
|4.||Insight and Prediction.||Analysis.|
|5.||It is used to predict the future.||It is used to analyze the past.|
|6.||Bottom-up approach.||Top-down approach.|
|7.||Discovery driven.||Query driven.|
|8||It is a emerging technique.||Widely Used.|
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- OLAP Guidelines (Codd's Rule)
- Types of Sources of Data in Data Mining
- Data Normalization in Data Mining
- Data Preprocessing in Data Mining
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