A novel approach to adaptive relational association rule mining

Czibula, Gabriela; Czibula, Istvan Gergely; Sirbu, Adela-Maria; Mircea, Ioan-Gabriel

doi:10.1016/j.asoc.2015.06.059

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…AGRARM is an adaptation of ARARM [8] so as to additionally consider the degree to which the rules are satisfied. This implies that the rules AGRARM discovers as interesting are also filtered according to a given minimum membership threshold (see Function isInteresting in Section 3) in addition to support and confidence minimum thresholds.…”

Section: 1mentioning

confidence: 99%

“…Subsequently, Adaptive Relational Association Rule Mining (ARARM ) [8] has been proposed as a method for adapting the set of all interesting RARs discovered within a data set before extending its features set, so as to obtain all interesting RARs within the extended data set.…”

Section: Introductionmentioning

confidence: 99%

“…We propose, therefore, Adaptive Gradual Relational Association Rules Miner (AGRARM ), which is an algorithm that adapts the set of all interesting GRARs mined before extension so as to obtain all interesting GRARs that characterize the extended data. AGRARM is the equivalent of ARARM [8], but for mining GRARs instead of RARs, within a dynamic data set.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Gradual Relational Association Rules Mining Approach

Miholca

2018

Studia UBB Informatica

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This paper focuses on adaptive Gradual Relational Association Rules mining. Gradual Relational Association Rules capture gradual generic relations among data features. We propose AGRARM , an algorithm for mining the interesting Gradual Relational Association Rules characterizing a data set that has been extended with a number of new attributes, through adapting the set of interesting rules mined before extension, so as to preserve the completeness. We aim, through AGRARM , to make the mining process more efficient than resuming the mining algorithm on the enlarged data. We have experimentally evaluated AGRARM versus mining from scratch on three publicly available data sets. The obtained reduction in mining time highlights AGRARM 's efficiency, thus confirming the potential of our proposal.

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Section: 1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Gradual Relational Association Rules Mining Approach

Miholca

2018

Studia UBB Informatica

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“…In [37], based on involved criteria and covered examples by association rules, a new measure is presented to evaluate the similarity between two rules and a new genetic algorithm is provided to obtain a reduce set of different positive and negative quantitative association rules. In [38], an adaptive relational association rule mining method is proposed to discover interesting relational association rules from the set of extracted association rules, which was established by mining the data before the feature set changed and preserving the completeness. As our best knowledge, association rule mining algorithms based on support and confidence measures are well-established and widely used in large datasets, such as Apriori [1], Eclat [39], FP-Growth [40] or LCM [41] algorithms, adding others interesting measures in these algorithms to mine satisfied association rules generally face memory space or response time problem.…”

Section: Introductionmentioning

confidence: 99%

Selection of Satisfied Association Rules via Aggregation of Linguistic Satisfied Degrees

Ren

Pei

2019

IEEE Access

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Many association rule mining algorithms have been well-established, such as Apriori, Eclat, FP-Growth, or LCM algorithms. However, the challenge is that the huge size of association rules is extracted by using these algorithms, and it is difficult for users to select satisfied association rules from them. In this paper, a new method is proposed to select satisfied association rules, which is based on the aggregation of fuzzy linguistic satisfied degrees of extracted association rules. To this end, two problems must be solved, one is which interesting measures are utilized to obtain fuzzy linguistic satisfied degrees of association rules and the other is how to aggregate them. For the first problem, many objective and subjective interesting measures have been proposed, which are generally included in [0, 1] or others universes and easily calculated by support, confidence, or other measures, these interesting measures cannot be directly aggregated, because different interesting measures represent different satisfied degrees of association rules. In this paper, a new transformation function is proposed to transform these interesting measures into fuzzy linguistic satisfied degrees, such as dissatisfied, fair, satisfied, and so on. For the second problem, by considering different weights of objective and subjective interesting measures, linguistic aggregation operators are designed to aggregate these fuzzy linguistic satisfied degrees of association rules. Accordingly, satisfied association rules are selected by using order on the aggregation results of linguistic satisfied degrees. In cases' study, Apriori, Eclat, FP-Growth, and LCM algorithms are first utilized to extract association rules with higher support or confidence measures from Chess, Connect, Mushroom, and T40I10D100K databases, then the proposed method is applied to obtain fuzzy linguistic satisfied degrees of extracted association rules and aggregate and select satisfied association rules from those extracted association rules, and comparison and analysis show that the proposed method is a useful and alternative tool to select satisfied association rules from extracted association rules.INDEX TERMS Association rule mining, objective interesting measures, subjective interesting measures, fuzzy linguistic satisfied degree, linguistic aggregation operator.

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“…Most of time, it is not the sample vectors as integrity shows the strong coherence with each other, but the elements at some specific positions among different sample vectors show the local similarity (Valarmathi et al , 2015). Besides classical clustering methods such as hierarchical clustering, in recent years, biclustering has become a popular approach to analyze biological data sets and a wide variety of algorithms, and analysis methods have been published (Czibula et al , 2015; Shinde and Kulkarni, 2016; Indira and Kanmani, 2015).…”

Section: Introductionmentioning

confidence: 99%

Application of biclustering algorithm to extract rules from labeled data

Zhang

Sun

2018

IJCS

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Purpose For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times. Design/methodology/approach In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed. Findings To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters. Originality/value The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

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A novel approach to adaptive relational association rule mining

Cited by 10 publications

References 32 publications

An Adaptive Gradual Relational Association Rules Mining Approach

An Adaptive Gradual Relational Association Rules Mining Approach

Selection of Satisfied Association Rules via Aggregation of Linguistic Satisfied Degrees

Application of biclustering algorithm to extract rules from labeled data

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