Data mining just means gathering information. The purpose of data mining is to collect as much information as possible about any particular issue so that analysts can spot trends and predict what is likely to happen next. It is useful for companies because it helps them tailor their goods and services for the market.
data mining is very important because the particular user will be looking for a pattern not for complete data in the database,it is better to read wanted data than unwanted data.........and other advantage by data mining technique is that only required pattern will be drawn from database with in short time....
One of the data mining tool is :SAS tool(statistical analysis system)
catch important data from data warehouse.
CHARECTERISTICS OF DATA MINING CHARECTERISTICS OF DATA MINING
mining the data is called data mining. Mining the text is called text mining
Some seminar topics related to data mining could include: Introduction to data mining techniques and algorithms Applications of data mining in business intelligence Big data analytics and data mining Ethical considerations in data mining and privacy protection.
Data Mining companies provide such services as mining for data and mining for data two electric bugaloo. They will often offer to resort to underhanded tactics to mine said data.
Data mining can uncover interesting patterns. Some cookies will upload solely for the purpose of data mining.
Data warehouse is the database on which we apply data mining.
Simply, Data mining is the process of analyzing data from several sources and converting it into useful data.
One can learn about data mining by visiting the data mining wikipedia page, which has a very comprehensive article about the topic, starting with the etymology and mostly talking about the various uses of data mining.
Data mining
difference between Data Mining and OLAP
Directed data mining involves using predefined goals or objectives to guide the analysis and modeling of data. In contrast, undirected data mining aims to discover patterns or relationships in data without specifying a particular outcome in advance. Directed data mining is typically used for tasks such as classification and regression, while undirected data mining techniques include clustering and anomaly detection.