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What Kinds of data can be mined?
Data mining defines extracting or mining knowledge from huge amounts of data. Data mining is generally used in places where a huge amount of data is saved and processed. For example, the banking system uses data mining to save huge amounts of data which is processed constantly.
In Data mining, hidden patterns of data are considering according to the multiple categories into a piece of useful data. This data is assembled in an area including data warehouses for analyzing it, and data mining algorithms are performed. This data facilitates in creating effective decisions which cut value and increase revenue.
There are various types of data mining applications that are used for data are as follows −
Relational Databases − A database system is also called a database management system. It includes a set of interrelated data, called a database, and a set of software programs to handle and access the data.
A relational database is a set of tables, each of which is authorized a unique name. Each table includes a set of attributes (columns or fields) and generally stores a huge set of tuples (records or rows). Each tuple in a relational table defines an object identified by a unique key and represented by a set of attribute values. A semantic data model including an entity-relationship (ER) data model is generally constructed for relational databases. An ER data model defines the database as a set of entities and their relationships.
Transactional Databases − A transactional database includes a file where each record defines a transaction. A transaction generally contains a unique transaction identity number (trans ID) and a list of the items creating up the transaction (such as items purchased in a store).
The transactional database can have additional tables related to it, which includes other data regarding the sale, including the date of the transaction, the customer ID number, the ID number of the salesperson and of the branch at which the sale appeared, etc.
Object-Relational Databases − Object-relational databases are assembled based on an object-relational data model. This model continues the relational model by supporting a rich data type for managing complex objects and object orientation.
Temporal Database − A temporal database generally stores relational data that contains time-related attributes. These attributes can include multiple timestamps, each having several semantics.
Sequence Database − A sequence database stores sequences of ordered events, with or without a factual idea of time. For example, customer shopping sequences, Web click streams, and biological sequences.
Time-Series Database − A time-series database stores sequences of values or events accessed over repeated measurements of time (e.g., hourly, daily, weekly). An example includes data collected from the stock exchange, stock control, and the measurement of natural phenomena (like temperature and wind).
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