Emerging Trends in Associative Classification Data Mining

Abdelhamid, Neda; Ayesh, Aladdin; Thabtah, Fadi

doi:10.12720/ijeee.3.1.50-53

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…For example, in a rule such as Pes1, Pes2 → C1, C1 must be a class attribute, while Pes1 and Pes2 are attribute values. This rule can be interpreted as follows: if the Pes1 and Pes2 attribute values appear together for any instance, this instance can be classified as C1 [20,22,23]. In our approach, when implementing the delegation process, the rule (Peo1 → Pes1) & (Pes1→ C1), Pes1 → C1 is used and can be interpreted as follows: if the attribute Peo1 is mapped to attribute Pes1 and Peos1 is classified as C1, then Peo1 can be classified as C1.…”

Section: The Proposed Modelmentioning

confidence: 99%

Short Text Mining for Classifying Educational Objectives and Outcomes

Asiri¹

2022

Computer Systems Science and Engineering

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Most of the international accreditation bodies in engineering education (e.g., ABET) and outcome-based educational systems have based their assessments on learning outcomes and program educational objectives. However, mapping program educational objectives (PEOs) to student outcomes (SOs) is a challenging and time-consuming task, especially for a new program which is applying for ABET-EAC (American Board for Engineering and Technology the American Board for Engineering and Technology-Engineering Accreditation Commission) accreditation. In addition, ABET needs to automatically ensure that the mapping (classification) is reasonable and correct. The classification also plays a vital role in the assessment of students' learning. Since the PEOs are expressed as short text, they do not contain enough semantic meaning and information, and consequently they suffer from high sparseness, multidimensionality and the curse of dimensionality. In this work, a novel associative short text classification technique is proposed to map PEOs to SOs. The datasets are extracted from 152 selfstudy reports (SSRs) that were produced in operational settings in an engineering program accredited by ABET-EAC. The datasets are processed and transformed into a representational form appropriate for association rule mining. The extracted rules are utilized as delegate classifiers to map PEOs to SOs. The proposed associative classification of the mapping of PEOs to SOs has shown promising results, which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.

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Section: The Proposed Modelmentioning

confidence: 99%

Short Text Mining for Classifying Educational Objectives and Outcomes

Asiri¹

2022

Computer Systems Science and Engineering

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“…In this a new feature selection method is proposed [1]. This paper enhances the performance of the associative algorithms or their quality in terms of rules [2]. Medical data mining has been utilized in the health care industry for the prediction of diseases.…”

Section: Related Workmentioning

confidence: 99%

Customer Relationship Management Classification by Hybridizing Genetic Algorithm and Fuzzy K-Nearest Neighbor - A Review

Kaur¹,

Bhatia²

2016

IJCA

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“…Meanwhile, the association process discovers the correlations between attributes in the dataset. In the past few years, researchers have adopted a new technique called association classification (AC) to classify the accuracy of the classification process (Abdelhamid et al, 2014;Abdelhamid et al, 2015;Abdallat et al, 2019). The AC technique aims to build a classifier from a large labeled dataset, referred to a training data, in order to predict the class value of unseen instances, referred to as test data (Abu-Mansour et al,2012;Tan et al, 2006;Hadi, 2013;Abdelhamid et al, 2015;Salah et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Technique to Predict the Autism Spectrum Disorder Using Big Data Platform

Alwidian¹

2023

MAS

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Autism or autism spectrum disorder (ASD) is considered a psychiatric disorder. It is a condition that puts constraints on the use of linguistic, cognitive, communicative, and social skills and abilities. Recently, many data mining techniques have been developed to help autism patients by discovering the main features of the condition and the correlation between them. In this paper, we employ the association classification (AC) technique as a data mining approach to predict whether or not an individual has an autism. The Intelligent Classification Based on Association rules (ICBA) algorithm is proposed for finding the correlations between the features to decide whether an individual has autism in its early stage, especially in childhood. The ICBA algorithm incorporates the chi-square method to select the best feature to make the decision, in addition to proposing new techniques in all phases and increasing number of folds to 2size of data/10. The proposed algorithm is compared against four well-known AC algorithms in terms of accuracy to evaluate their behavior in the prediction task using big data platform. The results show a better performance for the ICBA algorithm in most experiments. Moreover, all of the considered algorithms had an increased level of accuracy when the chi-square method was used.

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Emerging Trends in Associative Classification Data Mining

Cited by 8 publications

References 6 publications

Short Text Mining for Classifying Educational Objectives and Outcomes

Short Text Mining for Classifying Educational Objectives and Outcomes

Customer Relationship Management Classification by Hybridizing Genetic Algorithm and Fuzzy K-Nearest Neighbor - A Review

An Intelligent Technique to Predict the Autism Spectrum Disorder Using Big Data Platform

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