Efficient Frequent Patterns Based on Incremental Mining Model for Uncertain Databases
A database is viewed as a set of deterministic instances. Possible world refers the finite number of tuples with finite attributes. Uncertain database contains an exponential number of possible worlds. Rule mining algorithms are used to extract frequent item sets. The candidate sets and item sets are extracted from the database using the attribute name and attribute values. The support values are used to filter frequent rules. The incremental mining models are designed to refresh candidate set generation process. The candidate set values are updated with reference to the insert, delete and update operations. Uncertain databases are maintained with exponential number of tuples and attributes. Probabilistic mining techniques are used for the uncertain databases.