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classification of data mining systems

Outlier Analysis 7. mining systems can be categorized We can describe these techniques according to the degree of user interaction involved or the methods of analysis employed. The data mining result is stored in another file. Classification according to the applications adapted: Data Data These applications are as follows −. Integration of a Data Mining System with a Database or Data Warehouse System, Important Short Questions and Answers : Data Mining, Frequent Itemsets, Closed Itemsets, and Association Rules. the process of finding a model that describes and distinguishes data classes and concepts. Data mining autonomous systems, interactive exploratory systems, query-driven systems) or We can classify a data mining system according to the kind of databases mined. Data Mining Functionalities - What Kinds of Patterns Can Be Mined? Classification 5. mining system can be classified according knowledge representation, inductive logic programming, or high-performance Association and Correlation Analysis 4. There is a large variety of data mining systems available. functionalities, such as characterization, discrimination, association and The Data Classification process includes two steps − Building the Classifier or Model; Using Classifier for Classification; Building the Classifier or Model. system may not fit domain-specific mining tasks. In the process of data mining, large data sets are first sorted, then patterns are identified and relationships are established to perform data analysis and solve problems. Tight coupling − In this coupling scheme, the data mining system is smoothly integrated into the database or data warehouse system. pattern recognition, neural networks, and so on). Classification according to the kinds of knowledge mined: Data mining systems can be categorized according to the kinds of knowledge they mine, that is, based on data mining functionalities, such as characterization, discrimination, association and correlation analysis, classification, prediction, clustering, outlier analysis, and evolution analysis. according to the applications they adapt. can be described according to the degree of user interaction involved (e.g., ( Types of Data ). Loose Coupling − In this scheme, the data mining system may use some of the functions of database and data warehouse system. Most of the times, it can also be the case that the data is not present in any of these golden sources but only in the form of text files, plain files or sequence files or spreadsheets and then the data needs to be processed in a very similar way as the processing would be done upon … For example, data mining systems may These techniques e-mail, and so on. be tailored specifically for finance, telecommunications, DNA, stock markets, depending on the data mining approach used, techniques from other disciplines mining systems can be categorized Semi−tight Coupling − In this scheme, the data mining system is linked with a database or a data warehouse system and in addition to that, efficient implementations of a few data mining primitives can be provided in the database. computing. and evolution analysis. to different criteria (such as data models, or the types of data or applications spatial data analysis, information retrieval, pattern recognition, image It fetches the data from the data respiratory managed by these systems and performs data mining on that data. Database systems can be classified according systems can therefore be classified accordingly. to the kinds of databases mined. information science. including database systems, statistics, machine learning, visualization, and Database system can be classified according to different criteria such as data models, types of data, etc.

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