Applying Domain Knowledge in Association Rules Mining Process – First Experience

Rauch, Jan; Šimůnek, Milan

doi:10.1007/978-3-642-21916-0_13

Cited by 10 publications

(13 citation statements)

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“…We use a logical calculus ST of association rules inspired by data set STULONG 1 [30] of patients to illustrate particular languages and procedures of FOFRADAR. Calculus ST has attributes A 1 , .…”

Section: Logical Calculus St Of Association Rulesmentioning

confidence: 99%

“…, f 20 of type T ST by M ST . Let us note that a data matrix Entry is used in papers [28,30] concerning experiments with parts of FOFRADAR. Data matrix Entry has 64 columns and 1417 rows.…”

Section: Logical Calculus St Of Association Rulesmentioning

confidence: 99%

“…Several aspects of an approach to formalization of a data mining process with association rules are introduced in [17,19,20,21,30]. The resulting formal framework is called FOFRADAR (FOrmal FRAmework for Data mining with Association Rules).…”

Section: Introductionmentioning

confidence: 99%

“…We use definitions and considerations scattered in relatively short papers [17,19,20,21,30] and we present them here in more detail together with new features of FOFRADAR. New features concern a language of data knowledge (section 5), a detailed definition of a language formulas which define a set of relevant association rules (section 7.1), a procedure assigning formulas of this language to analytical questions (section 7.2), and conclusions concerning solved analytical questions (section 10).…”

Section: Introductionmentioning

confidence: 99%

“…Successful experiments with implementation of its core steps are presented in [27,28,30]. The experiments are based on the LISp-Miner system [34,35] and on the 4ft-Miner procedure [25] which is an enhanced implementation of the ASSOC procedure.…”

Section: Introductionmentioning

confidence: 99%

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Formal Framework for Data Mining with Association Rules and Domain Knowledge – Overview of an Approach

Rauch

2015

Fundamenta Informaticae

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A formal framework for data mining with association rules is introduced. The framework is based on a logical calculus of association rules which is enhanced by several formal tools. The enhancement allows the description of the whole data mining process, including formulation of analytical questions, application of an analytical procedure and interpretation of its results. The role of formalized domain knowledge is discussed.We deal with association rules ϕ ≈ ψ where ϕ and ψ are general Boolean attributes derived from columns of analysed data matrices. Data matrices are results of observation of a finite number of objects. Rows of a data matrix correspond to observed objects, columns correspond to observed attributes. There is a finite number of possible values (i.e. categories) of each attribute. Principles of our approach to association rules and calculi of association rules are informally introduced in sections 2.1.1 -2.1.3. Let us note that detailed description of logical calculi of association rules is provided in [23]. Boolean AttributesBasic Boolean attributes are defined first. Basic Boolean attribute is an expression A(α) where A is an attribute and α is a subset of its categories. Expressions Sex(F ) and Education(secondary, university) are examples of basic Boolean attributes, see logical calculus ST of association rules inspired by a real data set introduced in section 2. Basic Boolean attributea value of the column of M corresponding to the attribute A in the row o. Each basic Boolean attribute is a Boolean attribute. If κ and λ are Boolean attributes then ¬κ, κ ∧ λ, and κ ∨ λ are Boolean attributes. Expression Sex(F) ∧ Education(secondary, university) and Diabetes(yes) ∨ Infarction(yes) are examples of Boolean attributes, see also section 2.2. Truthfulness of Boolean attributes ¬κ, κ ∧ λ, and κ ∨ λ is defined in a usual way. This means that ¬κ is true in a row o of a data matrix M if and only if κ is false in o, κ ∧ λ is true in o if and only if both κ and λ are true in o, and κ ∨ λ is true in o if and only if κ or λ are true in o.The symbol ≈ used in ϕ ≈ ψ is called 4ft-quantifier. A function F ≈ mapping a set of all quadruples a, b, c, d of non-negative integer numbers satisfying a + b + c + d > 0 into the set {0, 1} is associated to each 4ft-quantifier ≈. Examples of 4ft-quantifiers and their associated functions F ≈ are given in section 2.3. There are about forty 4ft-quantifiers defined and studied in [23]. Associated functions of some of them are defined such that suitable thresholds are used for various measures of interestingness of association rules as defined e.g. in [5]. The additional 4ft-quantifiers correspond to statistical hypotheses tests. The association rule ϕ ≈ ψ is true in a data matrix M if and only if it is F ≈ (a, b, c, d) = 1 where a, b, c, d = 4f t(ϕ,ψ, M) and F ≈ is the associated function of ≈. J. Rauch / Formal Framework for Data Mining with Association Rules and Domain Knowledge -Overview 175 2.1.3 . Calculus of Association RulesDeduction rules of the form ϕ≈ψ ϕ ≈ψ where ...

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Section: Logical Calculus St Of Association Rulesmentioning

confidence: 99%

“…, f 20 of type T ST by M ST . Let us note that a data matrix Entry is used in papers [28,30] concerning experiments with parts of FOFRADAR. Data matrix Entry has 64 columns and 1417 rows.…”

Section: Logical Calculus St Of Association Rulesmentioning

confidence: 99%