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What is the classification of data mining systems?
Data mining refers to extracting or mining knowledge from large amounts of data. Data mining is generally used in places where a huge amount of data is saved and processed.
Data mining is an interdisciplinary field, the assemblage of a set of disciplines, such as database systems, statistics, machine learning, visualization, and data science. It is depending on the data mining approach used, techniques from other disciplines may be applied, such as neural networks, fuzzy and/or rough set theory, knowledge representation, inductive logic programming, or high-performance computing.
It is established on the types of data to be mined or on the given data mining application, the data mining system can also integrate methods from spatial data analysis, data retrieval, pattern identification, image analysis, signal processing, computer graphics, network technology, economics, business, bioinformatics, or psychology.
The classification of data mining is as follows −
Classification according to the kinds of databases mined − A data mining system can be classified according to the kinds of databases mined. Database systems can be classified according to various criteria (including data models, or the types of data or applications contained), each of which can need its data mining technique.
For example, if classifying according to data models, it can have a relational, transactional, object-relational, or data warehouse mining system. If classifying according to the special types of data handling, we may have a spatial, time-series, text, stream data, multimedia data mining system, or a World Wide Web mining system.
Classification according to the kinds of knowledge mined − Data mining systems can be categorized according to the kinds of knowledge they mine. It is based on data mining functionalities, including characterization, discrimination, association, and correlation analysis, classification, prediction, clustering, outlier analysis, and evolution analysis. A data mining system generally supports multiple and integrated data mining functionalities.
Classification according to the kinds of techniques utilized − Data mining systems can be categorized according to the fundamental data mining techniques employed. These techniques can be described according to the degree of user interaction involved in autonomous systems, interactive exploratory systems, query-driven systems, or the methods of data analysis employed.
Classification according to the applications adapted − Data mining systems can also be categorized according to the applications they adapt. For instance, data mining systems can be tailored categorically for finance, telecommunications, DNA, stock markets, e-mail, etc. There are multiple applications often needed the integration of application-specific methods.
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