Advanced data mining software provides highly accurate results when used with quality datasets and appropriate algorithms. Accuracy in most cases ranges from 85-95% depending on the quality of the data, algorithm selection, and proper parameters tuning, respectively. Always validate results with cross-validation techniques.

Many free data mining tools are available for processing huge data sets, although premium data mining tools usually have better performance in terms of big data. Open source tools such as R and Python libraries can process millions of records, while commercial software optimizes processing for enterprise-scale data.

The current data mining programs provide both visual user interfaces and programming capabilities. Users who are interested in business can use drag-and-drop and ready-to-use templates, and those interested in technical operations can use Python, R, or SQL to customize algorithms to perform advanced analysis.

Processing time varies based on dataset size, complexity, and chosen algorithms. Simple analyses on small datasets can be completed within minutes, while complex machine learning models on large datasets may require hours. Most tools provide progress indicators and can run analyses in the background.

Professional data mining tools support multiple formats, including CSV, Excel, JSON, XML, database connections (SQL, NoSQL), cloud storage, APIs, and streaming data sources. This flexibility ensures compatibility with existing data infrastructure and workflows.