BibTeX:

@article{IJIRSTV2I2074,
     title={Efficient Mining of Frequent Patterns from Uncertain Databases using Hierarchical Agglomerative Clustering},
     author={Sujeena S and Mary Mareena P V },
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={2},
     pages={234--237},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I2074.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

In many real time applications such as sensors monitoring systems, location based systems etc data uncertainty is inherent. The uncertainty may occur as a result of evaluation errors. This uncertainty becomes a major issue while performing mining operations in the databases. Traditional mining methods are less efficient when dealing with uncertain databases. While considering uncertain databases one of the major issues is to mine frequent item sets from database. Here some algorithms which are used to perform frequent pattern mining in uncertain databases are provided. These algorithms discussed here are the extension of the frequent pattern mining algorithms which are used in deterministic databases where the values are precise. These algorithms are modified in such a way to handle uncertainty in the database. Among the various algorithms the U-prefix span algorithm an extension of prefix span algorithm gives better performance. Clustering is used in this algorithm to further increase the efficiency.

uncertain database; frequent sequential patterns; PWS; hierarchical agglomerative clustering