Mining for 4ft Association Rules

Rauch, Jan; Šimůnek, Milan

doi:10.1007/3-540-44418-1_27

Cited by 25 publications

(14 citation statements)

References 3 publications

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“…In this sense, there is a relation with the work of [Rauch and Simunek 2001], where the notion of conditional association rules has been used to analyze a dataset of loans. A conditional rule A ⇔ C/B denotes a context B in which itemsets A and C are equivalent, namely where conf (A, B → C) = 1 and conf (¬A, B → ¬C) = 1.…”

Section: Extended Liftmentioning

confidence: 99%

“…However, we can say nothing about conf (B → C), and, consequently, about the relative strength of the rule with respect to the base classification rule. In addition to ⇔, the 4ft-Miner system [Rauch and Simunek 2001; allows for the extraction of conditional rules with other operators. The "above average dependence" operator defines rules A ∼ + C/B such that supp(A, B, C) ≥ ms, where ms is the minimum support threshold, and lif t B (A → C) ≥ 1 + p, where B = {T ∈ D | B ⊆ T } is the set of transactions verifying B.…”

Section: Extended Liftmentioning

confidence: 99%

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Data mining for discrimination discovery

Ruggieri

Pedreschi

Turini

2010

ACM Trans. Knowl. Discov. Data

131

118

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In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Discrimination in credit, mortgage, insurance, labor market, and education has been investigated by researchers in economics and human sciences. With the advent of automatic decision support systems, such as credit scoring systems, the ease of data collection opens several challenges to data analysts for the fight against discrimination. In this paper, we introduce the problem of discovering discrimination through data mining in a dataset of historical decision records, taken by humans or by automatic systems. We formalize the processes of direct and indirect discrimination discovery by modelling protected-by-law groups and contexts where discrimination occurs in a classification rule based syntax. Basically, classification rules extracted from the dataset allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-bylaw groups is formalized by an extension of the lift measure of a classification rule. In direct discrimination, the extracted rules can be directly mined in search of discriminatory contexts. In indirect discrimination, the mining process needs some background knowledge as a further input, e.g., census data, that combined with the extracted rules might allow for unveiling contexts of discriminatory decisions. A strategy adopted for combining extracted classification rules with background knowledge is called an inference model. In this paper, we propose two inference models and provide automatic procedures for their implementation. An empirical assessment of our results is provided on the German credit dataset and on the PKDD Discovery Challenge 1999 financial dataset.

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Section: Extended Liftmentioning

confidence: 99%

Section: Extended Liftmentioning

confidence: 99%

Data mining for discrimination discovery

Ruggieri

Pedreschi

Turini

2010

ACM Trans. Knowl. Discov. Data

131

118

View full text Add to dashboard Cite

show abstract

“…Technically, we measured discrimination through a generalization of lift to cope with contexts, specified as non-discriminatory itemsets. In this sense, there is a relation with the work of [13], where the notion of conditional association rules has been studied. A conditional rule A ⇔ C/B denotes a context B in which itemsets A and C are equivalent, namely where conf (A, B → C) = 1 and conf (¬A, B → ¬C) = 1.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Discrimination-aware data mining

Pedreshi

Ruggieri

Turini

2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

537

433

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In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Rules extracted from databases by data mining techniques, such as classification or association rules, when used for decision tasks such as benefit or credit approval, can be discriminatory in the above sense. In this paper, the notion of discriminatory classification rules is introduced and studied. Providing a guarantee of non-discrimination is shown to be a non trivial task. A naïve approach, like taking away all discriminatory attributes, is shown to be not enough when other background knowledge is available. Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge. An empirical assessment of the results on the German credit dataset is also provided.

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“…These measures are usually defined using the 2 × 2 contingency table presented in figure 1, and is a classical way of measuring association in the case of paired attributes [23], such as in the Guha method [21], in the 4ft-Miner tool [80] and in the Apriori algorithm [2].…”

Section: Interestingness Measuresmentioning

confidence: 99%

Association Rule Interestingness Measures: Experimental and Theoretical Studies

Lenca¹,

Vaillant²,

Meyer

et al. 2007

Quality Measures in Data Mining

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Summary.It is a common problem that Kdd processes may generate a large number of patterns depending on the algorithm used, and its parameters. It is hence impossible for an expert to assess these patterns. This is the case with the wellknown Apriori algorithm. One of the methods used to cope with such an amount of output depends on using association rule interestingness measures. Stating that selecting interesting rules also means using an adapted measure, we present a formal and an experimental study of 20 measures. The experimental studies carried out on 10 data sets lead to an experimental classification of the measures. This study is compared to an analysis of the formal and meaningful properties of the measures. Finally, the properties are used in a multi-criteria decision analysis in order to select amongst the available measures the one or those that best take into account the user's needs. These approaches seem to be complementary and could be useful in solving the problem of a user's choice of measure.

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Mining for 4ft Association Rules

Cited by 25 publications

References 3 publications

Data mining for discrimination discovery

Data mining for discrimination discovery

Discrimination-aware data mining

Association Rule Interestingness Measures: Experimental and Theoretical Studies

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