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Frequent Itemsets Mining on Large Uncertain Databases: Using Rule Mining Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 65 - Number 20 |
| Year of Publication: 2013 |
| Authors: Jency Varghese, K.soundararajan |
10.5120/11038-6181
|
Jency Varghese, K.soundararajan . Frequent Itemsets Mining on Large Uncertain Databases: Using Rule Mining Algorithm. International Journal of Computer Applications. 65, 20 ( March 2013), 6-10. DOI=10.5120/11038-6181
Abstract
In recent years, due to the wide applications of uncertain data mining frequent item sets over uncertain databases has attracted much attention. In uncertain databases, the support of an item set is a random variable instead of a fixed occurrence counting of this itemset. The data manipulated from sensor monitoring system and data integration diligence is highly ambiguous. One of the major issues is extracting frequent itemsets from a large uncertain database, interpreted under the Possible World Semantics. An uncertain database contains an exponential number of possible worlds, by observing that the mining process can be modeled as a Poisson binomial distribution. Mining such manifold Itemsets from generous ambiguous database illustrated under possible world semantics is a crucial dispute. Approximated algorithm is established to ascertain manifold Itemsets from generous ambiguous database exceedingly. This paper proposes Rule mining algorithm, which enable probabilistic frequent itemset results to be refreshed incase of update, delete and insert operations and also criticize the support for incremental mining and ascertainment of manifold Itemsets. Tuple and Attribute ambiguity is reinforced. Incremental Mining Algorithm is adduced to retain the mining consequence.
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