Post by etikhatun669911 on May 1, 2024 20:45:29 GMT -8
Data mining is a tool that enables us to make predictions that help guide an organization toward its goals through accurate decisions. Understanding the data mining process makes it easier to choose a good data mining solution, which is the first step to business improvement . ID-100123071 Photo credit: "The arrows around the red questions show the choices" by Stuart Miles Things to consider when choosing a data mining system Other data mining tools must be selected Canadian Hospitals Email List after evaluating their characteristics. The most important points to consider are: Data types: Not all data are the same, and not all tools on the market are ready for them, so you have to check the exact formats that your chosen data mining system can work with. The broadest examples include formatted text, record- based data, and relational data. Compatibility between systems: Data mining tools have to interact with existing operating systems, so this must be taken into account.
Sometimes compatibility is reduced to one option, while in some cases it is extended to multiple systems. Finally, it is necessary to know that there are also data mining systems that provide a Web -based user interface and allow the use of XML data as input. Data sources: The more general the data mining functions applicable to obtaining information from different sources, the more interesting it is. If these options are limited, it is not possible to face the heterogeneity of sources, which is common in this process. Functionality and methods: Some data mining systems offer a single function, such as classification, while others offer multiple functions, such as description, OLAP analysis, discovery, statistical analysis, classification, prediction, clustering, or search for similarities. It is not necessary to choose the one with the most functions, but the one that has all the functions you need.
Coupled with a database or data storage system: The data exploration system must be supplemented by a data storage or database system. Coupled components are integrated into a unified information processing environment. This coupling can occur in different ways, as follows: No coupling. Weak coupling. Semi-watertight coupling. Waterproof connector. 6. Scalability: This quality can be assessed from two different perspectives: Row scalability (database size): A data exploration system is said to be scalable when the number of rows expands by a certain proportion and the system takes no more time than the estimated time to execute the query (proportional increase). Column (dimension) scalability: A data exploration system is considered scalable if query execution time increases linearly with the number of columns. Visualization tools: A data mining solution should be chosen for the visibility it provides to identify or display the entire process based on data and results. Graphical User Interface: Finally, we must evaluate the interactivity the solution allows and its simplicity of use. Related articles: Predictive analytics algorithms and their practical applications Predictive analytics strategies and their benefits Big data and predictive analytics: the power of union.