Association rules mining algorithm based on Rough Set

Jiao, Xun; Xu, Liancheng; Lin, Qian

doi:10.1109/itime.2012.6291318

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Rough set theory is used in association rule discovery. It simplifies the process of traditional association rule mining and avoids redundant rules introduced in [22] to determine rough set rules. In the work presented in [23], a collection methodology was presented for mapping PEOs to SOs derived from the SSRs of 32 engineering programs accredited by ABET.…”

Section: Related Workmentioning

confidence: 99%

The Design of Academic Programs Using Rough Set Association Rule Mining

Hogo

2022

Applied Computational Intelligence and Soft Computing

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Program accreditation is important for determining whether or not a program or institution meets quality standards. It helps employers to evaluate the programs and qualifications of their graduates as well as to achieve its strategic goals and its continuous improvement plans. Preparing for accreditation requires extensive effort. One of the required documents is the program’s self-study report (SSR), which includes the PEO-SO map (which allocates the program’s educational objectives (PEOs) to student learning outcomes (SOs)). It influences program structure design, performance monitoring, assessment, and continuous improvement. Professionals in each academic engineering program have designed their PEO-SO maps in accordance with their experiences. The problem with the incorrect design of map design is that the SOs are either missing altogether or cannot be assigned to the correct PEOs. The objective of this work is to use a hybrid data mining approach to design the correct PEO-SO map. The proposed hybrid approach utilizes three different data mining techniques: classification to find the similarities between PEOs, crisp association rules to find the crisp rules for the PEO-SO map, and rough set association rules to find the coarse association rules for the PEO-SO map. The work collected 200 SSRs of accredited engineering programs by the ABET-EAC. The paper presents the different phases of the work, such as data collection and preprocessing, building of three data mining models (classification, crisp association rules, and rough set association rules), and analysis of the results and comparison with related work. The validation of the obtained results by different fifty specialists (from the academic engineering field) and their recommendations were also presented. The comparison with other related works proved the success of the proposed approach to discover the correct PEO-SO maps with higher performance.

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Section: Related Workmentioning

confidence: 99%

The Design of Academic Programs Using Rough Set Association Rule Mining

Hogo

2022

Applied Computational Intelligence and Soft Computing

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“…Definition 1: [7] Information system: It is composed of an ordered four-tuple, namely: IS = {U, R, V, f}, where U is the set of objects, and R is the collection of all attributes of the object. V = U r⊆R V r , V R in the formula is the value domain of the attribute r. f: U × R → V means that a specific value is assigned to the object from the attribute domain Information function.…”

Section: Basic Knowledgementioning

confidence: 99%

“…V = U r⊆R V r , V R in the formula is the value domain of the attribute r. f: U × R → V means that a specific value is assigned to the object from the attribute domain Information function. Definition 2: [7] Decision system: Information system with decision attributes. That is, DS = {U, C ∪ D, V, F} and C ∩ D = φ.…”

Section: Basic Knowledgementioning

confidence: 99%

“…Among them, U is a finite non-empty domain, C = {c1, c2, …, cm} is a set of conditional attributes, D is a set of decision-making attributes, and D = φ. Definition 3: [7] Equivalence relationship: R is a binary relation on the set X, and the equivalent relation R needs to satisfy the following three properties at the same time:…”

Section: Basic Knowledgementioning

confidence: 99%

“…Rough set [3][4][5][6] is a set of theories proposed by Professor Z. Pawlak of the Polish University of Technology. Attribute reduction [7,8] is one of the core contents of the rough set. Its role is to eliminate redundant attributes under the premise that the nature of the original decision table does not change to obtain a reduced decision table.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Association Rules Mining Algorithm Based on Information Gain Ratio Attribute Reduction

Han

Wang

Guo

et al. 2021

Business Intelligence and Information Technology

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In actual association rule mining, data sets collected from enterprises or real life often have some problems, such as a large amount of data missing or data redundancy, which greatly increases the spatial complexity of mining association rules and makes mining efficiency inefficient. Not only that, some actual data set contain hundreds or even more attributes. Not only does it take too long to mine association rules, but there are too many association rules obtained, making it difficult for users to distinguish which is more valuable information in practical applications. It is difficult to apply these data to actual enterprises to get greater benefits. In response to these problems, this paper proposes an association rule algorithm based on the FP-Growth association rule algorithm of information gain ratio attribute reduction to extract more valuable information and improve the efficiency of association rule mining. Finally, through experiments and comparisons, it is verified that the algorithm proposed in this paper can effectively mine the association rule information of multi-attribute data sets.

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