Data mining, or knowledge discovery in databases is the nontrivial extraction of implicit, previously unknown and potentially useful information from data. It is the search for relationships and global patterns that exist in large databases but are hidden among the vast amount of data. Using particular classifications, association rules and analyzing sequences, data is extracted, analyzed and presented graphically

iPrevent’s data mining algorithms provides a number of different technical approaches to address data cleaning, sampling, clustering, learning classification rules, analyzing changes and detecting anomalies. And, as described earlier, iPrevent’s data mining can automatically generate business rules from the customer’s data.

iPrevent Data Ming Technology automatically find patterns in large amounts of data. In most cases, the data consist of a large number of observations, where each observation represents a single object (e.g., a person, account, or wire transfer) and consists of values for each of several numeric or symbolic variables.  The iPrevent technology is a very complete suite, it include among others tools for:

1. Statistics Tools
2. Graphical visualization
3. Data Preparation
4. Field Matching
5. Pruning
6. Data Cleaning Tools

iPrevent’s suite of data mining algorithms provides a number of different technical approaches to address data cleaning, sampling, clustering, learning classification rules, analyzing changes and detecting anomalies. iPrevent’s data mining can automatically generate business rules from the customer’s data.