Considerations on Logical Calculi for Dealing with Knowledge in Data Mining

Rauch, Jan

doi:10.1007/978-3-642-02190-9_9

Cited by 15 publications

(13 citation statements)

References 15 publications

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“…al., 2004]), Set-differs-from-set (SD) rules or for 4ft-actional rules (see [Ras & Wieczorkowska, 2000], [Rauch & Šimůnek, 2009b]). This rich syntax makes possible to involve semantically features of logical reasoning and deduction ( [Rauch, 2009]). …”

Section: Task Buildermentioning

confidence: 99%

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EverMiner - towards Fully Automated KDD Process

Šimůnek¹,

Rauch²

2011

New Fundamental Technologies in Data Mining

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Section: Task Buildermentioning

confidence: 99%

“…Sets of heuristics and rules for datamining process automation were proposed and a theoretical logical backgrounds were established (see e.g. , [Rauch, 2009]). Distributed solving of data mining tasks using the computer grid was implemented (see [Šimůnek & Tammisto, 2010]).…”

mentioning

confidence: 99%

EverMiner - towards Fully Automated KDD Process

Šimůnek¹,

Rauch²

2011

New Fundamental Technologies in Data Mining

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“…In frequent subgroup mining, as neighbour field to association mining, background knowledge on causality was used to discover causal links among subgroups [10]. In our SEWEBAR project itself, an earlier established alternative thread aims at tighter integration between background knowledge representation and the actual data mining engine (LISp-Miner) [38], while the nature of background knowledge is largely overlapping with that of BKEF; particular attention is also paid to logical consequence computation between background knowledge and discovered association rules [37]. In all these (as well as the 'rule schema' methods introduced later), possibly sophisticated knowledge processing is not coupled with standardized data mining model representations and web-based report authoring support, as in SEWEBAR-CMS.…”

Section: Related Workmentioning

confidence: 99%

SEWEBAR-CMS: semantic analytical report authoring for data mining results

et al. 2010

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SEWEBAR-CMS is a set of extensions for the Joomla! Content Management System (CMS) that extends it with functionality required to serve as a communication platform between the data analyst, domain expert and the report user. SEWEBAR-CMS integrates with existing data mining software through PMML. Background knowledge is entered via a web-based elicitation interface and is preserved in documents conforming to the proposed Background Knowledge Exchange Format (BKEF) specification. SEWEBAR-CMS offers web service integration with semantic knowledge bases, into which PMML and BKEF data are stored. Combining domain knowledge and mining model visualizations with results of queries against the knowledge base, the data analyst conveys the results of the mining through a semiautomatically generated textual analytical report to the end user. The paper demonstrates the use of SEWEBAR-CMS on a real-world task from the cardiological domain and presents a user study showing that the proposed report authoring support leads to a statistically significant decrease in the time needed to author the analytical report.

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“…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%

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 ...

show abstract

Considerations on Logical Calculi for Dealing with Knowledge in Data Mining

Cited by 15 publications

References 15 publications

EverMiner - towards Fully Automated KDD Process

EverMiner - towards Fully Automated KDD Process

SEWEBAR-CMS: semantic analytical report authoring for data mining results

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

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