to find the unseen pattern in large volume of historical data that helps to mange an organization efficiently.
-Sequence or path analysis
-Classification
-Clustering
-Forecasting
to have a detailed study of any form of data. like in web its web mining- to study structure of web sites
Data mining can uncover interesting patterns. Some cookies will upload solely for the purpose of data mining.
difference between Data Mining and OLAP
The term data mining is generally known as the process of analyzing data from many different perspectives in order to correctly organize the data. Sometimes data mining is also called knowledge dicovery.
Data mining software is a practical way to look for patterns and correlations. Basically, data mining take out information from data and transform it in a way to be understood for future use.
Data mining is effectively storing and analysing old pieces of data and predicting what's going to happened in future based on trends and patterns in that data.
CHARECTERISTICS OF DATA MINING CHARECTERISTICS OF DATA MINING
mining the data is called data mining. Mining the text is called text mining
spatial data mining time series data mining text or multimedia data mining www mining systems
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 warehouse is the database on which we apply data mining.
Data mining can uncover interesting patterns. Some cookies will upload solely for the purpose of 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
Objective
The term data mining is generally known as the process of analyzing data from many different perspectives in order to correctly organize the data. Sometimes data mining is also called knowledge dicovery.