Difference between data mining and OLAP.

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 Data mining 

 Data mining refers to mining knowledge from a huge amount of data. In other words, you can say that data mining means gathering information and assembling data from various areas like data warehouses and data mining algorithms, searching for trends, patterns that business organizations can use to uplift customer service, thereby increasing their profit.


Properties of data mining

These are the key properties of data mining

  • Finding patterns automatically
  • Focus on huge datasets and databases
  • Predict the outcomes
  • Creation of actionable information


OLAP

  • OLAP stands for Online Analytical Processing. It is a computing method that allows users to extract useful information and query data in order to analyze it from different angles. For example, OLAP business intelligence queries usually aid in financial reporting, budgeting, predict future sales, trends analysis and other purposes. It enables the user to analyze database information from different database systems simultaneously. OLAP data is stored in multidimensional databases.
  • OLAP and data mining look similar since they operate on data to gain knowledge, but the major difference is how they operate on data. OLAP tools provide multidimensional data analysis and a summary of the data.


Key features of OLAP

  • It supports complex calculations
  • Time intelligence
  • It has a multidimensional view of data
  • Business-focused calculations
  • Flexible and self-service reporting

Applications of OLAP

  • Database Marketing
  • Marketing and sales analysis

The difference between data mining and OLAP are as follows:- 

Data mining

  • Data mining refers to the field of computer science, which deals with the extraction of data, trends, and patterns from huge sets of data.
  • It deals with the data summary.
  • It is discovery-driven.
  • It is used for future data prediction.
  • It has huge numbers of dimensions.
  • Bottom-up approach.
  • It is an emerging field.

OLAP
  • OLAP is a technology of immediate access to data with the help of multidimensional structures.
  • It deals with detailed transaction-level data.
  • It is query-driven.
  • It is used for analyzing past data.
  • It has a limited number of dimensions.
  • Top-down approach.
  • It is widely used.





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