Select the four data mining techniques.

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BlackTom AI · Accepted Answer

When assessing which items among the given options are recognized as data mining techniques, I’ll evaluate each term in the list.
Option 1: cluster analysis. This is a classic data mining technique used to group similar data points into clusters based on shared characteristics, without requiring labeled outcomes. It fits the standard set of data mining methods.
Option 2: estimation analysis. At first glance, this term resembles forecasting or statistical estimation, which can be part of data analytics, but in the strict sense of data mining techniques, it is not typically listed as a core technique. It’s more of a broader data analysis concept rather than a defined mining technique like clustering or classification.
Option 3: classification analysis. Classification is one of the foundational data mining techniques, where data are labeled and new instances are assigned to categories based on learned models. This is a well-established method in data mining.
Option 4: competitive monitoring. This phrase refers to observing competitors and market conditions, which is more of a business intelligence activity than a standard data mining technique. It describes an application area rather than a core mining method.
Option 5: identity management. This term relates to authentication and access control in IT systems, not to data mining processes. It lies outside the realm of data mining techniques.
Option 6: affinity grouping analysis. This label resembles affinity analysis, which is closely related to association rule mining used to discover relationships between items in large datasets. It can be considered a data mining technique, though the name here is less conventional.
From this evaluation, some options align with established data mining techniques (cluster analysis, classification analysis, and affinity-related mining), while others do not clearly fit the standard techniques or are not typically categorized as data mining methods.
In summary, the options that correspond to recognized data mining techniques include cluster analysis and classification analysis, with related concepts like affinity/grouping approaches adjacent to data mining. The others align more with broader BI, IT security, or non-mining domains, and thus are not standard data mining techniques.

Select the four data mining techniques.

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