Post by account_disabled on Feb 22, 2024 6:38:51 GMT -5
Data mining provides the link between transaction and independent analysis systems in the world of large-scale information technology . The different types of data mining software that can be found on the market analyze the relationships and patterns that occur in users' open queries. They do this by applying statistical techniques , machine learning and neural networks that seek to detect: Classes: used to locate data in predetermined groups among the data already stored. Clusters : they help detect consumption affinities, the precursor of market segments according to logical grouping relationships. Associations: interdependencies between data that serve to identify a trend or help understand a habit. Sequential patterns.
They reveal chained behaviors or situations that occur in a cascade on a high percentage of occasions. Its application to the business world has infinite possibilities that, in any of its aspects, are based on a better and deeper knowledge of clients, their habits and their motivations. data mining Photo credits: Elements of data mining Data mining comprises five main elements : 1. ETL processes: extract , transform and load transactional data into data storage systems . 2. Data warehouse: collect and manage data in a multidimensional Chinese Student Phone Number List database system . 3. Data access: provide data access to business analysts , information technology professionals and authorized users. 4. Data Analysis: Analyze data from all applications. 5. Presentation : of the data in a useful format , such as a graph or a table, that facilitates a good interpretation of its content. Analysis is a substantial part of data mining but it is not always carried out in the same way. In fact, there are different levels of analysis available, among which data visualizations stand out , which are based on graphic models; artificial neural networks , which learn through training; decision trees, which generate rules for classifying data sets; or genetic algorithms , which are nothing more than optimization techniques that use processes such as genetic combination , mutation and natural selection in a design based on the concepts of natural evolution.
The technological infrastructure that data mining requires Today , data mining applications are available for all systems and pockets. However, when it comes to diving into all the offerings available to any organization, there are two critical technological factors to take into account : Database size: the more data that needs to be processed and the greater volume of information that needs to be maintained, the more power the system will require . Query complexity: in the same way as with size, the greater the volume of queries launched and the higher their level of complexity, the more power will be required . Knowing both variables is the best starting point for a successful choice, as far as data mining systems are concerned. Of course, these factors are not the only ones to consider since, for example, talking about technological infrastructure is impossible without mentioning aspects such as scalability, which would be another quality to look for. Resources on Business Intelligence and Information Management Related posts: The solution to data quality problems used for analysis Data quality for analytics: problems and solutions Data mining: applications that are already a reality.
They reveal chained behaviors or situations that occur in a cascade on a high percentage of occasions. Its application to the business world has infinite possibilities that, in any of its aspects, are based on a better and deeper knowledge of clients, their habits and their motivations. data mining Photo credits: Elements of data mining Data mining comprises five main elements : 1. ETL processes: extract , transform and load transactional data into data storage systems . 2. Data warehouse: collect and manage data in a multidimensional Chinese Student Phone Number List database system . 3. Data access: provide data access to business analysts , information technology professionals and authorized users. 4. Data Analysis: Analyze data from all applications. 5. Presentation : of the data in a useful format , such as a graph or a table, that facilitates a good interpretation of its content. Analysis is a substantial part of data mining but it is not always carried out in the same way. In fact, there are different levels of analysis available, among which data visualizations stand out , which are based on graphic models; artificial neural networks , which learn through training; decision trees, which generate rules for classifying data sets; or genetic algorithms , which are nothing more than optimization techniques that use processes such as genetic combination , mutation and natural selection in a design based on the concepts of natural evolution.
The technological infrastructure that data mining requires Today , data mining applications are available for all systems and pockets. However, when it comes to diving into all the offerings available to any organization, there are two critical technological factors to take into account : Database size: the more data that needs to be processed and the greater volume of information that needs to be maintained, the more power the system will require . Query complexity: in the same way as with size, the greater the volume of queries launched and the higher their level of complexity, the more power will be required . Knowing both variables is the best starting point for a successful choice, as far as data mining systems are concerned. Of course, these factors are not the only ones to consider since, for example, talking about technological infrastructure is impossible without mentioning aspects such as scalability, which would be another quality to look for. Resources on Business Intelligence and Information Management Related posts: The solution to data quality problems used for analysis Data quality for analytics: problems and solutions Data mining: applications that are already a reality.