Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.
It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database.
Data Mining is similar to Data Science. It is carried out by a person, in a particular situation, on a specific data set, with an objective. This phase contains several types of services including text mining, web mining, audio and video mining, pictorial data mining, and social media mining. It is completed through software that is simple or greatly specific.
By outsourcing data mining, all the work can be done quicker with low operation costs. Specific firms can also use new technologies to save data that is impossible to find manually. There are tonnes of data available on multiple platforms, but very limited knowledge is accessible.
The major challenge is to analyze the data to extract essential data that can be used to solve an issue or for company development. There are many dynamic instruments and techniques available to mine data and discover better judgment from it.
Data mining is also known as Knowledge Discovery in Database (KDD). Knowledge discovery as a process includes an iterative series of the following steps −
Data cleaning − It can eliminate noise and inconsistent information.
Data integration − In data integration, where several data sources can be connected.
Data selection − In data selection, where data relevant to the analysis function are fetched from the database.
Data transformation − In data transformation, where data are transformed or linked into forms applicable for mining by executing summary or aggregation operations.
Data mining − It is an important phase where intelligent methods are used to extract data patterns.
Pattern evaluation − It can recognize the truly interesting patterns defining knowledge based on some interesting measures.
Knowledge presentation − In knowledge presentation, where visualization and knowledge representation methods are used to current the mined knowledge to the customer.