The difference between data mining and data warehousing

Data mining technology is the automatic or semi-automated method of mining and analysis of a large number of data to create effective models and rules, and enterprises through data mining can better understand their customers, and thus improve their marketing, business and customer service operations. Data mining is an important application of data Warehouse. Basically, it is used to dig out the information hidden in your data, so data Mining is actually part of the so-called knowledge Discovery, and data Mining uses a lot of statistical analysis and Modeling methods, Find useful features (Patterns) and connectivity (relationships) in the data. The process of knowledge Discovery has a significant impact on the success of data Mining, and only it ensures that data Mining can achieve meaningful results.

Data mining and OLAP as an analysis tool, the difference is that OLAP provides users with a convenient multi-dimensional views and methods, in order to efficiently perform complex query actions on the data, and its preset query conditions are pre-set by the user, and data mining, the information system can actively explore the source of the information is not found in the hidden information, and to generate information through the user's knowledge.

Data mining is a branch of computer science that involves extracting from large datasets. These processes combine the use of statistical methods and artificial intelligence. Data mining transforms raw data into the source of artificial intelligence in modern enterprises. Manipulate data so that it provides reliable information that can be used for decision-making. This gives companies a big advantage in their competition and can rely on their data sets to provide intelligence. Data mining is also organized in analysis practices including marketing, monitoring science, and detecting fraudulent behavior.

There are other common terminology associated with data mining, such as data fishing, data snooping, and so on. All of these point to different data mining applications to sample smaller datasets for production statistics and inference.

Data Warehouse can be used as data mining and OLAP analysis tools, because the data stored in the warehouse, must be filtered and converted, so you can avoid the analysis tool to use the wrong data, and get incorrect analysis results.

On the other hand, a data warehouse is a term that describes a collection of data that a system uses in an organization. These data are collected in a data warehouse that provides transactional systems such as invoices, purchase records, and even loan records. Data records for each point are created and then assembled together, which is the Data warehouse. Data reports from the Data Warehouse can help users with business information and make effective decisions.

Summarize:

Data mining is the process of extracting data from a large amount of data.
A data warehouse is a process of pooling all relevant data.
Both data mining and data warehousing are collections of business intelligence tools.
Data mining is a specific collection of data.
A data warehouse is a tool to save time and improve efficiency by organizing data from different locations and regions.
Data Warehouse layer three, which is segmented, integrated, and accessed.

The difference between data mining and data warehousing