With clustering (a K-means algorithm) Datameer automatically finds non-obvious but related groups within your data by automating the process of identifying and measuring common attributes within the dataset. The obvious benefit is that if you can segment your data into groups, you can treat the groups differently.Learn More
Datameer’s decision trees help you understand the different combinations of data attributes that result in a desired outcome. Decision trees are often used when enriching a dataset with additional data sources to optimize a process for a better outcome. The structure of the decision tree reflects the structure that is possibly hidden in your data.Learn More
Want to know how strongly a single data attribute like age, location, or gender, relates to other data attributes like income, college degree, or credit score? The column dependency algorithm automatically compares every possible data attribute combination and visually ranks the strengths of those relationships so you can instantly see where to focus further. Those relationships are important themselves and is often used to help target further analysis.
Datameer’s recommendation engine automatically predicts interests of a person based on historical observations of similar people’s interests so you can increase engagement, recommend more relevant choices, increase customer satisfaction, and more.Learn More