The SVM algorithm – what is it and how to use it?
In today's data-heavy world, analysts are often faced with the challenge of how to effectively extract valuable information from data.
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Discriminant analysis
Discriminant analysis is one of the statistical techniques used to classify objects based on their features.
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Task diagram – new possibilities for creating and visualizing processes in PS CLEMENTINE PRO
Creating and managing analytical tasks is an important part of working with ETL (Extract, Transform and Load) tools and advanced data analysis programs.
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Sequential rule induction
Sequential rule induction is an advanced data mining technique that enables the discovery of patterns occurring in sequences of events.
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Basket analysis: application and characteristics
Basket analysis is a popular data mining technique, primarily used for the content of shopping baskets in retail, marketing, and e-commerce.
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Rule induction algorithms – discovering patterns in data
Rule induction is one of the key methods in the field of artificial intelligence and machine learning. It enables the automatic extraction of patterns and relationships from data. This technique involves analysing data sets to formulate general rules that describe the relationships between variable…
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Auto Classification – automatic model selection for data in PS CLEMENTINE PRO
When working with data, the analyst is often faced with the challenge of selecting appropriate statistical tests to provide valuable answers to the research questions posed. PS CLEMENTINE PRO can provide a solution to this problem. This tool offers a wide range of modelling methods that are based o…
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Automatic preparation of data for analysis, part II
Preparing data for analysis, as has been said many times on this blog, is a key part of analysis. It is often a time-consuming and difficult process that even experienced data analysts can find difficult. In this article, we return to the issue of automatic data preparation in PS CLEMENTINE PRO. P…
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Automatic preparation of data for analysis
Data preparation plays a key role in data analysis and machine learning processes. Its importance stems from several important aspects that affect the quality and reliability of the results. High-quality data influences more accurate and reliable statistical models. Raw, unprocessed data often cont…
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