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What is the History of Data Mining?
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 approach of finding useful patterns in data has been given several names, containing data mining, knowledge extraction, data discovery, data harvesting, data archaeology, and data pattern processing. Data mining has been used by statisticians, data analysts, and the management information systems (MIS) communities.
It has also improved popularity in the database area. The process of knowledge discovery in databases was invented at the first KDD workshop in 1989 (Piatetsky-Shapiro 1991) to maintain that knowledge is the end product of data-driven discovery. It has been popularized in the artificial intelligence and machine learning areas.
KDD defines the complete process of discovering useful knowledge from data, and data mining defines a specific step in this process. Data mining is the application of specific algorithms for extracting patterns from data. The difference between the KDD process and the data-mining step (within the process) is a central point of this object.
The further steps in the KDD process, including data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper analysis of the results of mining, are important to provide that useful knowledge is changed from the data.
Blind application of data-mining techniques (rightly disapproved as data dredging in the statistical literature) can be a dangerous activity, easily leading to the discovery of meaningless and invalid designs.
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