Issue No. 12 - Dec. (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.165
Liang Wang , The University of Hong Kong, Hong Kong
David Wai-Lok Cheung , The University of Hong Kong, Hong Kong
Reynold Cheng , The University of Hong Kong, Hong Kong
Sau Dan Lee , The University of Hong Kong, Hong Kong
Xuan S. Yang , The University of Hong Kong, Hong Kong
The data handled in emerging applications like location-based services, sensor monitoring systems, and data integration, are often inexact in nature. In this paper, we study the important problem of extracting frequent item sets from a large uncertain database, interpreted under the Possible World Semantics (PWS). This issue is technically challenging, since an uncertain database contains an exponential number of possible worlds. By observing that the mining process can be modeled as a Poisson binomial distribution, we develop an approximate algorithm, which can efficiently and accurately discover frequent item sets in a large uncertain database. We also study the important issue of maintaining the mining result for a database that is evolving (e.g., by inserting a tuple). Specifically, we propose incremental mining algorithms, which enable Probabilistic Frequent Item set (PFI) results to be refreshed. This reduces the need of re-executing the whole mining algorithm on the new database, which is often more expensive and unnecessary. We examine how an existing algorithm that extracts exact item sets, as well as our approximate algorithm, can support incremental mining. All our approaches support both tuple and attribute uncertainty, which are two common uncertain database models. We also perform extensive evaluation on real and synthetic data sets to validate our approaches.
Itemsets, Approximation algorithms, Data mining, Uncertainty, Mobile radio mobility management, incremental mining, Frequent item sets, uncertain data set, approximate algorithm
X. S. Yang, R. Cheng, S. D. Lee, L. Wang and D. W. Cheung, "Efficient Mining of Frequent Item Sets on Large Uncertain Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 2170-2183, 2012.