Mining fuzzy association rules from uncertain data

Weng, Cheng-Hsiung; Chen, Yen-Liang

doi:10.1007/s10115-009-0223-1

Cited by 39 publications

(14 citation statements)

References 45 publications

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“…The authors in [14] proposed a representation scheme to characterize uncertain data based on possibility distributions. The possibility theory establishes a close connection between the concepts of similarity and uncertainty, providing an excellent framework for handling uncertain data.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

Gabr¹,

Darwish²,

Mohsin³

2015

LNSE

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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 M 2 TQT (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 M 2 TQT 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.

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Section: Literature Surveymentioning

confidence: 99%

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

Gabr¹,

Darwish²,

Mohsin³

2015

LNSE

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“…Solutions for frequent itemset mining in uncertain transaction databases have been investigated in [3,12,42,44]. In [42], an approach for summarizing frequent itemset patterns based on Markov Random Fields has been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…In [42], an approach for summarizing frequent itemset patterns based on Markov Random Fields has been proposed. In [3,12,44], efficient frequent pattern mining algorithms based on the expected support counts of the patterns have been developed. However, [39,46] found that the use of expected support may render important patterns missing.…”

Section: Related Workmentioning

confidence: 99%

Model-based probabilistic frequent itemset mining

et al. 2012

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Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of imprecise information, uncertain databases have been recently developed. In this paper, we study how to efficiently discover frequent itemsets from large uncertain databases, interpreted under the Possible World Semantics. This is technically challenging, since an uncertain database induces an exponential number of possible worlds. To tackle this problem, we propose a novel methods to capture the itemset mining process as a probability distribution function taking two models into account: the Poisson distribution and the normal distribution. These model-based approaches extract frequent itemsets with a high degree of accuracy and support large databases. We apply our techniques to improve the performance of the algorithms for (1) finding itemsets whose frequentness probabilities are larger than some threshold and (2) mining itemsets with the k highest frequentness probabilities. Our approaches support both tuple and attribute uncertainty models, which are commonly used to represent uncertain databases. Extensive evaluation on real and synthetic datasets shows that our methods are highly accurate and four orders of magnitudes faster than previous approaches. In further theoretical and experimental studies, we give an intuition which model-based approach fits best to different types of data sets.

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“…Association rule mining, which was first proposed by Agrawal et al [8], is one of the most important topics in the area of data mining, and has many successful applications, especially in the analysis of consumer market-basket data [6], [17]. An association rule is a probabilistic relationship, with the form X ⇒ Y between sets of database attributes, where X and Y are sets and termed as itemsets, and x y = Φ.…”

Section: B Association Rulesmentioning

confidence: 99%

An associative watermarking based image authentication scheme

Bakrawy

Ghali

Hassanien

et al. 2010

2010 10th International Conference on Intelligent Systems Design and Applications

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Abstract-In this paper, we propose an associative watermarking scheme which is conducted by the concept of Association Mining Rules (AMRs) and the ideas of Vector Quantization (VQ) and Soble operator. Performing associative watermarking rules to the images will reduct the amount of the embedded data, and using VQ indexing scheme can easily recall the embedded watermark for the purpose of image authentication, and establishing the relation between the association rules on both the original image and the watermark image. The Vector Quantization decoding technique is applied to reconstruct the watermarked image from the watermarked index table. The experimental result shows that the proposed scheme is robust. When the watermarked images suffered from various kinds of image-processing procedures, such as Gaussian noise, brightness, blurring, sharpening, cropping, and JPEG lossy compression can be detected without the original images assistance.

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Mining fuzzy association rules from uncertain data

Cited by 39 publications

References 45 publications

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

Model-based probabilistic frequent itemset mining

An associative watermarking based image authentication scheme

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