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An Improvised on Mining Frequent Item Sets on Large Uncertain Databases


Prof. Smt. Sushama Anantrao Deshmukh , Government College of Engineering, Aurangabad, Maharashtra state, India


Uncertain dataset, Frequent item sets, Approximate algorithm, Incremental mining, PFI, pmf


Data processed in emerging applications, such as site-based services, sensor monitoring systems and data integration, are often inaccurate. In this paper, the important problem of extracting sets of frequent objects from a large uncertain database, interpreted under the possible World Seminar (PWS) is presented. This problem is technically difficult because an uncertain database contains an exponential number of possible worlds. By observing that the mining process can be modeled as a binomial distribution of Poisson, an algorithm has been developed, which makes it possible to discover efficiently and precisely sets of frequent objects in a very uncertain database. The important issue of maintaining the mining result for a scalable database (e.g. by inserting a tuple) can be presented. More precisely, the proposed exploration algorithm can refresh the probabilistic results of the set of frequent objects (PFI). This reduces the need to re-run the entire extraction algorithm on the new, often more expensive and unnecessary database. The proposed algorithm can support progressive extraction and provides accurate results on uncertain database extraction. The in-depth evaluation of the actual data defined to validate the approach is carried out.

Other Details

Paper ID: IJSRDV5I41586
Published in: Volume : 5, Issue : 4
Publication Date: 01/07/2017
Page(s): 1769-1772

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