Various terms in Data Mining
Data mining has applications in multiple fields like science and research. It is a prediction based on likely outcomes. Its focuses on the last data set. Data mining is the procedure of mining knowledge from data. The knowledge extracted so can be used for any of the following applications such as production control, market analysis, science exploration. Data mining is the practice of searching large stores of data to discover patterns. It trends that go beyond simple analysis. Data mining is also known as knowledge. It focuses on the last data sets and databases and the creation of actionable information. It is the automatic discovery of patterns.
Data mining deals with a kind of pattern that can be mined. There are such categories as descriptive, classification, prediction, cluster analysis, evolution analysis.
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- Knowledge base :
The knowledge base is domain language. It is used to guide the search and interestingness of resulting patterns. It is knowledge presentation, data integration etc.
- Data transformation :
Data is transformed into forms appropriate for mining, by performing summary operations.
- Clusters :
To a group of similar kinds of objects. Cluster forming group of objects that are very similar to each other.
- Data cleaning :
It is a process of preparing data for data mining activities. The technology that applied to remove the data and correct the consistency is in data. It performed as a data pre-processing step.;
- Data selection :
It is the process where data relevant to analysis task are retrieved from the data.
- Data integration :
Data integration is the data processing technique. We use it to merge the data from multiple heterogeneous data.
- User interface :
It visualizes the pattern in the module of data mining system that help the communication between users and data. It’s providing information to help focus the search.
- Data :
It defined as facts, transactions, and figures.
- GUI :
Graphical user Interface.
- Data mining :
It extracts the information from the youth set of data. This information for the following applications market analysis, science exploration, production control.
- Associations :
It is a type of algorithm. To create rules that describe how events have together.
- Classification :
It referred to the data mining problem. To predict the category of categorical data by building a model. It must be based on some predictor variables.
- Continuous :
It can have any value in an interval of real numbers. The value does not have to be an integer continuous is the opposite categorical.
- DBMS :
Database management systems.
- Interaction :
When two independent variable interest and changes in the value of one change the effect on the dependent variable of the others.