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What is the difference between Data Mining and Data Warehouse?
Data Mining
Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.
In Data mining, hidden patterns of data are considered according to the multiple categories into a piece of useful data. This data is assembled in an area including data warehouses for analyzing it, and data mining algorithms are performed. This data facilitates in creating effective decisions which cut value and increase revenue.
Data mining is an important method where previously unknown and potentially useful data is extracted from a huge amount of information. The data mining process contains several components, and these components constitute a data mining system structure.
Data Warehouse
Data Warehousing is a technique that is mainly used to collect and manage data from various sources to give the business a meaningful business insight. A data warehouse is specifically designed to support management decisions.
In simple terms, a data warehouse defines a database that is maintained independently from an organization's operational databases. Data warehouse systems enable the integration of multiple application systems. They provide data processing by offering a solid platform of consolidated, historical information for analysis.
Data warehouses generalize and centralize data in multidimensional space. The construction of data warehouses contains data cleaning, data integration, and data transformation and can be looked at as an important preprocessing step for data mining.
It provides online analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data generalization and data mining. There are several data mining functions, including association, classification, prediction, and clustering can be integrated with OLAP operations to build up interactive mining of knowledge at various levels of abstraction.
Let us see the comparison between Data Mining and Data Warehouses.
| Data Mining | Data Warehouse |
|---|---|
| Data mining is usually treated as the procedure of extracting useful data from a huge set of data. | Data warehousing is the phase of combining all the relevant information. |
| The benefit of the data mining approach is the detection and identification of the unwanted errors that appear in the system. | The benefit of the data warehouse is its capacity to update frequently. The main reason is that it is ideal for business entrepreneurs who need up-to-date with the current stuff. |
| Data mining is the phase of determining data patterns. | A data warehouse is a database system designed for analytics. |
| The data mining techniques are profitable as compared to different statistical data applications. | The authority of the data warehouse is to facilitate each type of business information. |
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