A New Method to Select the Interesting Association Rules with Multiple Criteria

Dahbi, Azzeddine; Jabri, Siham; Balouki, Youssef; Gadi, Taoufiq

doi:10.22266/ijies2017.1031.21

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…Data mining, also called knowledge discovery in databases, is an important research domain in computer science, it is widely used in business (insurance, retail, banking, credit card fraud detection system), science research (medicine, astronomy, biological data analysis), and government security (detection of criminals and terrorists). One of the most important DM tasks is to find the association rules and to discover the interesting and useful patterns and relationships in large volumes of data [19]. At first, the association rule theory was widely utilized for marketing aims, but it could also be used in different domains of research such as the searching of frequent values, pairs or cooccurrences if the data set is in conformity with this research, as Hébrail et al [20] posited.…”

Section: Data Miningmentioning

confidence: 99%

A New Partial 3D Object Indexing and Retrieval Approach Combining 2D slices and Apriori Algorithm

Taybi¹,

Gadi²,

Alaoui³

2020

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This paper examines the issue of 3D object indexing and retrieval and tries to solve this problem using partial indexing approach. The hypothesis in this context is that similar 3D objects will be composed of similar 2D slices. The proposed partial 3D object indexing and retrieval method is applicable on both complete and incomplete 3D objects, which is based on a similarity measuring between 2D slices of 3D objects. The main idea behind our approach is to extract an initial set of 2D slices corresponding to determined axes, and then use the Apriori algorithm to select the most representative ones, transforming the issue of shapematching between 3D objects into evaluating the similarities between their 2D slices. Experiments on the Princeton Shape Benchmark (PSB) indicate that our approach outperforms evaluated retrieval approaches.

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Section: Data Miningmentioning

confidence: 99%

A New Partial 3D Object Indexing and Retrieval Approach Combining 2D slices and Apriori Algorithm

Taybi¹,

Gadi²,

Alaoui³

2020

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“…Bruce et al [17] uses Wikipedia and its hyperlink structures to find related terms for reformulating a query using link probability weighting and link-based measure by counting the number of documents where the term is already a hyperlink divided by the number of documents where [6] for generating expansion terms. Second, relatedness between expansion terms and original query is using a new measure that combines a score based on the expansion graph, measure of an association rules technique based on multi-criteria optimization [18] and ESA.…”

Section: Related Workmentioning

confidence: 99%

“…As second step, we construct the transactional dataset by considering each keyword as item, each sentence as transaction and the document in which the sentence occurs as transaction elements. After we import a transactional dataset and we apply the referenced algorithm in [18]. It is based on ELECTRE [25] method which is able to select the most interesting association rules generated using Apriori [26] by considering a new outranking relation.…”

Section: Association Rules Similaritymentioning

confidence: 99%

Improving Retrieval Performance Based on Query Expansion with Wikipedia and Text Mining Technique

Jabri¹,

Dahbi²,

Gadi³

et al. 2018

IJIES

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Abstract:Textual query is the simple mean for communicating with a retrieval system. However, there is a risk of providing an incomplete query which hinders the system from satisfying the user information needs. By reformulating the queries, query expansion is solution for this problem, this mainly relies on an accurate choice of the added terms to an initial query. It can yield a large number of irrelevant terms, which in turn negatively influences quality of retained documents. In this paper, we propose Query Expansion approach. It consists of reformulating queries by semantically related terms extracted from a semantic graph called query graph derived from Wikipedia. Furthermore, we propose a similarity measure which computes the similarity between a candidate terms and initial query using the query graph, Explicit Semantic Analysis (ESA) measure, and text mining technique. The experiments on Text Retrieval Conference (TREC) collection show that the proposed approach performs significantly better than the baseline system and some existing techniques.

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“…However, it does not improve the framework of the association rules algorithm depends on the support and confidence. In [23] developed multicriteria decision-making approach depends on ELECTRE methodology. It selected the most utilized association rules produced utilizing apriori.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Discrimination Prevention and Rule Protection Algorithms Avoid Direct and Indirect Data Discrimination in Web Mining

Babu¹,

Pushpa²

2018

IJIES

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Discrimination is one of the most important challenging tasks in web mining due to its many legal and ethical features in social media and enterprise based industries. There are an enormous amount of anti-discrimination measures available to prevent discrimination such as using some features like race, religion, gender, nationality, disability, marital status, and age besides situations like employment and training, access to public services, credit, insurance, etc. Practically, those systems are not possible to use in industries due to large datasets. Indirect discrimination contains a set of rules or techniques which are not explicitly specifying discriminatory features, deliberately or accidentally and could create unfair decisions. Existing systems have low classification accuracy and data loss with high discrimination data detection time. To overcome these limitations, an Efficient Discrimination Prevention and Rule Protection (EDPRP) approach has been proposed for removing the discrimination and protects the rule without damaging the data quality. The proposed system designing pre-processing discrimination prevention approach and specify the different features and represent to deal with direct or indirect discrimination. EDPRP is capable of preventing Indirect and direct discrimination, and it allows automatic and routine collection of large amounts of data from the public. In EDPRP, the discrimination prevention model is based on partial data sets as part of the automated decision making. Based on Experimental evaluations, proposed method improves 8% (percentages) of Support and 8 ms (milliseconds) of Execution Time compared than existing methods.

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A New Method to Select the Interesting Association Rules with Multiple Criteria

Cited by 12 publications

References 15 publications

A New Partial 3D Object Indexing and Retrieval Approach Combining 2D slices and Apriori Algorithm

A New Partial 3D Object Indexing and Retrieval Approach Combining 2D slices and Apriori Algorithm

Improving Retrieval Performance Based on Query Expansion with Wikipedia and Text Mining Technique

An Efficient Discrimination Prevention and Rule Protection Algorithms Avoid Direct and Indirect Data Discrimination in Web Mining

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