Professor of Health Services Research at a university with 1,001-5,000 employees
Real User
2018-07-10T08:50:42Z
Jul 10, 2018
Safeguarding naive users against erroneous reporting from not knowing the statistical assumptions underlying a given technique i.e. I am agreeing with Nicholas Kogan.
After that, the order of importance of features depends on the use, and on who the user will be. The system does need to cover the whole workflow life-cycle. Fortunately, most of the widely-used systems do offer that.
Ability to import many different data sources across platforms. Reliable name in the industry. Good knowledgeable support staff. Good GUI. Good presentation ability.
Data Mining is a category of software solutions that enable organizations to extract valuable insights and patterns from large datasets. These tools utilize various algorithms and techniques to analyze data, identify trends, and make predictions.
Key features of data mining tools include:
Data preprocessing: Cleaning and transforming raw data for analysis.
Pattern discovery: Identifying hidden patterns and relationships within the data.
Predictive modeling: Building models to...
The most crucial aspects to consider when choosing a Data Mining tool include:
Integration Capabilities: Ensure it can connect with various databases, APIs, and other data sources.
Ease of Use: Look for intuitive interfaces and robust support resources.
Scalability: The tool should handle increasing amounts of data efficiently.
Performance: Assess the speed and accuracy of data processing and analysis.
Functionality: Verify that it includes essential data mining techniques like classification, regression, clustering, and association.
Customization: Ability to tailor features to specific business needs.
Support and Community: Availability of technical support, documentation, and an active user community.
Security: Robust data protection and compliance features.
Cost: Ensure it fits within the budget while meeting all essential requirements.
Safeguarding naive users against erroneous reporting from not knowing the statistical assumptions underlying a given technique i.e. I am agreeing with Nicholas Kogan.
After that, the order of importance of features depends on the use, and on who the user will be. The system does need to cover the whole workflow life-cycle. Fortunately, most of the widely-used systems do offer that.
Ability to import many different data sources across platforms. Reliable name in the industry. Good knowledgeable support staff. Good GUI. Good presentation ability.
Methodological transparency with accessible evaluation tools to prevent the black-box effect.
Ease of use to do cluster analysis as well as anomaly and dependency detection.