Automatic Extraction of Decision Rules from Non-deterministic Data Systems: Theoretical Foundations and SQL-Based Implementation

Ślęzak, Dominik; Sakai, Hiroshi

doi:10.1007/978-3-642-10583-8_18

Cited by 18 publications

(5 citation statements)

References 13 publications

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“…This rule generator is much faster than the previously implemented rule generator in SQL. The DIS-Apriori system consists of two new programs in Figure 1, the first one is a translation program (trans.py) from DIS to one common RDF format [22], and the second one is a rule generation program (disapri.py) based on the RDF format. In trans.py, the different characteristics (the number of attributes, the names of attributes, etc.)…”

Section: The Dis-apriori Algorithm and Some Improvementsmentioning

confidence: 99%

Rule Generation from Several Types of Table Data Sets and Its Application: Decision-Making with Transparency and an Improved Execution Environment

Sakai

Jian

2023

International Journal of Service and Knowledge Management

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This paper copes with rule generation from table data sets and applies the obtained rules to decision support. Here, two types of table data sets are considered. One type of them is specified as a Deterministic Information System (DIS). The other type is specified as a Non-deterministic Information System (NIS) for dealing with incomplete information. Two rule generation algorithms are refined and newly implemented in Python. Every obtained rule is applied as evidence of decision-making. Therefore, the reasoning process preserves its transparency, which will be an essential characteristic for Explainable AI. The decision support environment is strengthened due to some described improvements and is also brushed up in Python. Some running videos of Python are available on the web page. This framework applies to almost any table data sets, and we can generate rules from them. This framework based on discrete data will complement statistical data analysis based on numerical data.

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Section: The Dis-apriori Algorithm and Some Improvementsmentioning

confidence: 99%

Rule Generation from Several Types of Table Data Sets and Its Application: Decision-Making with Transparency and an Improved Execution Environment

Sakai

Jian

2023

International Journal of Service and Knowledge Management

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“…Based on [39, 40], we extend the RDF (resource description framework) format to the NRDF format. The RDF format may be termed the EAV (entity‐attribute‐value) format [41, 42].…”

Section: Nis‐apriori‐based Rule Generator In Sqlmentioning

confidence: 99%

Rough set‐based rule generation and Apriori‐based rule generation from table data sets: a survey and a combination

Sakai

Nakata

2019

CAAI Transactions on Intelligence Technology

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“…See e.g. [47] for further discussion. In [19,20], all possible cases of DISs that may be obtained from an incomplete information system were called extensions.…”

Section: Definitionsmentioning

confidence: 99%

“…As an example, there is a lot of relevant research on machine learning and data mining based on the rough set and the granular computing concepts [9,15,32,33,52,56]. In particular, we have been developing a formal framework for rule generation in NISs, both from mathematical [23,38,42] and software-related perspectives [41,43,47].…”

Section: Introductionmentioning

confidence: 99%

Apriori-Based Rule Generation in Incomplete Information Databases and Non-Deterministic Information Systems

Sakai

Nakata

2014

Fundamenta Informaticae

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This paper discusses issues related to incomplete information databases and considers a logical framework for rule generation. In our approach, a rule is an implication satisfying specified constraints. The term incomplete information databases covers many types of inexact data, such as non-deterministic information, data with missing values, incomplete information or interval valued data. In the paper, we start by defining certain and possible rules based on non-deterministic information. We use their mathematical properties to solve computational problems related to rule generation. Then, we reconsider the NIS-Apriori algorithm which generates a given implication if and only if it is either a certain rule or a possible rule satisfying the constraints. In this sense, NIS-Apriori is logically sound and complete. In this paper, we pay a special attention to soundness and completeness of the considered algorithmic framework, which is not necessarily obvious when switching . H. Sakai et al. / Apriori-Based Rule Generation in Incomplete Information Databasesfrom exact to inexact data sets. Moreover, we analyze different types of non-deterministic information corresponding to different types of the underlying attributes, i.e., value sets for qualitative attributes and intervals for quantitative attributes, and we discuss various approaches to construction of descriptors related to particular attributes within the rules' premises. An improved implementation of NIS-Apriori and some demonstrations of an experimental application of our approach to data sets taken from the UCI machine learning repository are also presented. Last but not least, we show simplified proofs of some of our theoretical results.

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Automatic Extraction of Decision Rules from Non-deterministic Data Systems: Theoretical Foundations and SQL-Based Implementation

Cited by 18 publications

References 13 publications

Rule Generation from Several Types of Table Data Sets and Its Application: Decision-Making with Transparency and an Improved Execution Environment

Rule Generation from Several Types of Table Data Sets and Its Application: Decision-Making with Transparency and an Improved Execution Environment

Rough set‐based rule generation and Apriori‐based rule generation from table data sets: a survey and a combination

Apriori-Based Rule Generation in Incomplete Information Databases and Non-Deterministic Information Systems

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