Data mining is defined as extracting the information from a huge set of data. In other words we can say that data mining is mining the knowledge from data. This information can be used for any of the following applications −
Data mining engine is very essential to the data mining system. It consists of a set of functional modules that perform the following functions −
This is the domain knowledge. This knowledge is used to guide the search or evaluate the interestingness of the resulting patterns.
Some people treat data mining same as knowledge discovery, while others view data mining as an essential step in the process of knowledge discovery. Here is the list of steps involved in the knowledge discovery process −
User interface is the module of data mining system that helps the communication between users and the data mining system. User Interface allows the following functionalities −
Data Integration is a data preprocessing technique that merges the data from multiple heterogeneous data sources into a coherent data store. Data integration may involve inconsistent data and therefore needs data cleaning.
Data cleaning is a technique that is applied to remove the noisy data and correct the inconsistencies in data. Data cleaning involves transformations to correct the wrong data. Data cleaning is performed as a data preprocessing step while preparing the data for a data warehouse.
Data Selection is the process where data relevant to the analysis task are retrieved from the database. Sometimes data transformation and consolidation are performed before the data selection process.
Cluster refers to a group of similar kind of objects. Cluster analysis refers to forming group of objects that are very similar to each other but are highly different from the objects in other clusters.
In this step, data is transformed or consolidated into forms appropriate for mining, by performing summary or aggregation operations.