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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), DOAJ, Electronic Journals Library, Engineering & Technology Digital Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2015 Vol.3(2): 157-163 IS4SN: 2301-3559
DOI: 10.7763/LNSE.2015.V3.182

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

Mahmoud M. Gabr, Saad M. Darwish, and Sayed A. Mohsin
Abstract—Many studies on association rule mining have focused on item sets from precise data in which the presence and absence of items in transactions was certainly known. In some applications, the presence and absence of items in transactions are uncertain and the knowledge discovered from this type of data will extracted with approximation manner. Data compression offers a good solution to reduce data size that can save the time of discovering useful knowledge. In this paper we suggest a new algorithm to compress transactions from uncertain database based on modified version of M2TQT (Mining Merged Transactions with the Quantification Table) approach and fuzzy logic concept. The algorithm bands the uncertain data to set of clusters using K-Mean algorithm and exploits fuzzy membership function to classify the transaction items as one of those clusters. Finally, the modified version of M2TQT has been employed to compress the classified transactions. The key idea of our algorithm is that since uncertain data is probabilistic in nature and frequent item set is counted as expected values so, compressed transactions will give us approximate values for the item set’s support. Experimental results show that the proposed algorithm is better than U-Apriori algorithm in case of large uncertain database.

Index Terms—Rule mining, database compression, Uncertain database, fuzzy logic.

Mahmoud M. Gabr is with the Department of Mathematics and Computer Sciences, Faculty of Science, Alexandria University, Egypt (e-mail: mahgabr@yahoo.com).
Saad M. Darwish is with Computer Science, Institute of Graduate Studies and Research (IGSR), Alexandria University, Egypt (e-mail: saad.darwish@alex-igsr.edu.eg).
Sayed A. Mohsin is with the IGSR, Alexandria University, Egypt, and is also with Amiral, Egypt (e-mail: sayed.abdelmohsin@amiral.com).


Cite: Mahmoud M. Gabr, Saad M. Darwish, and Sayed A. Mohsin, "An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems," Lecture Notes on Software Engineering vol. 3, no. 2, pp. 157-163, 2015.

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