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What is the motivation behind data mining?
Data mining is the procedure of finding useful new correlations, patterns, and trends by sharing 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.
It is not limited to the use of computer algorithms or statistical techniques. It is a process of business intelligence that can be used together with information technology to support company decisions.
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.
Data mining has engaged a huge deal of attention in the information market and society as a whole in current years, because of the wide availability of huge amounts of data and the imminent needed for turning such data into beneficial data and knowledge. The information and knowledge gained can be used for software ranging from industry analysis, fraud detection, and user retention, to production control and science exploration.
Data mining can be considered as a result of the natural progress of data technology. The database system market has supported an evolutionary direction in the development of the following functionalities including data collection and database creation, data management, and advanced data analysis.
For example, the recent development of data collection and database creation structure served as necessary for the later development of an effective structure for data storage and retrieval, and query and transaction processing. With various database systems providing query and transaction processing as common practice, advanced data analysis has developed into the next object.
Data can be saved in several types of databases and data repositories. One data repository structure that has appeared in the data warehouse, a repository of several heterogeneous data sources organized under a unified schema at an individual site to support management decision making.
Data warehouse technology involves data cleaning, data integration, and online analytical processing (OLAP), especially, analysis techniques with functionalities including summarization, consolidation, and aggregation, and the ability to view data from multiple angles.
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