2,979 research outputs found

    On NIS-Apriori Based Data Mining in SQL

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    We have proposed a framework of Rough Non-deterministic Information Analysis (RNIA) for tables with non-deterministic information, and applied RNIA to analyzing tables with uncertainty. We have also developed the RNIA software tool in Prolog and getRNIA in Python, in addition to these two tools we newly consider the RNIA software tool in SQL for handling large size data sets. This paper reports the current state of the prototype named NIS-Apriori in SQL, which will afford us more convenient environment for data analysis.International Joint Conference on Rough Sets (IJCRS 2016), October 7-11, 2016, Santiago, Chil

    An overview of decision table literature 1982-1995.

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    This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

    Division Charts as Granules and Their Merging Algorithm for Rule Generation in Nondeterministic Data

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    We have been proposing a framework rough Nondeterministic information analysis, which considers granular computing concepts in tables with incomplete and nondeterministic information, as well as rule generation. We have recently defined an expression named division chart with respect to an implication and a subset of objects. Each division chart takes the role of the minimum granule for rule generation, and it takes the role of contingency table in statistics. In this paper, we at first define a division chart in deterministic information systems (DISs) and clarify the relation between a division chart and a corresponding implication. We also consider a merging algorithm for two division charts and extend the relation in DISs to nondeterministic information systems. The relation gives us the foundations of rule generation in tables with nondeterministic information

    A Proposal of a Privacy-preserving Questionnaire by Non-deterministic Information and Its Analysis

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    We focus on a questionnaire consisting of three-choice question or multiple-choice question, and propose a privacy-preserving questionnaire by non-deterministic information. Each respondent usually answers one choice from the multiple choices, and each choice is stored as a tuple in a table data. The organizer of this questionnaire analyzes the table data set, and obtains rules and the tendency. If this table data set contains personal information, the organizer needs to employ the analytical procedures with the privacy-preserving functionality. In this paper, we propose a new framework that each respondent intentionally answers non-deterministic information instead of deterministic information. For example, he answers ‘either A, B, or C’ instead of the actual choice A, and he intentionally dilutes his choice. This may be the similar concept on the k-anonymity. Non-deterministic information will be desirable for preserving each respondent\u27s information. We follow the framework of Rough Non-deterministic Information Analysis (RNIA), and apply RNIA to the privacy-preserving questionnaire by non-deterministic information. In the current data mining algorithms, the tuples with non-deterministic information may be removed based on the data cleaning process. However, RNIA can handle such tuples as well as the tuples with deterministic information. By using RNIA, we can consider new types of privacy-preserving questionnaire.2016 IEEE International Conference on Big Data, December 5-8, 2016, Washington DC, US

    On the Range of the Risk-Free Interest Rate in Incomplete Markets

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    In a model of a two-period exchange economy under uncertainty, we find both upper and lower bounds for the risk free interest rate when the agents' utility functions exhibit constant absolute risk aversion. These bounds are independent of the degree of market incompleteness, and so in particular these results show to what extent market incompleteness can explain the risk-free rate puzzle in this class of general equilibrium models with heterogeneous agents. A general method of finding these bounds without the assumption of constant absolute risk aversion is also presented.The risk-free rate puzzle, constant absolute risk aversion, incomplete markets, general equilibrium.

    Granules for Association Rules and Decision Support in the getRNIA System

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    This paper proposes granules for association rules in Deterministic Information Systems (DISs) and Non-deterministic Information Systems (NISs). Granules for an association rule are defined for every implication, and give us a new methodology for knowledge discovery and decision support. We see that decision support based on a table under the condition P is to fix the decision Q by using the most proper association rule P〵Rightarrow Q. We recently implemented a system getRNIA powered by granules for association rules. This paper describes how the getRNIA system deals with decision support under uncertainty, and shows some results of the experiment

    NIS-Apriori-based rule generation with three-way decisions and its application system in SQL

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    In the study, non-deterministic information systems-Apriori-based (NIS-Apriori-based) rule generation from table data sets with incomplete information, SQL implementation, and the unique characteristics of the new framework are presented. Additionally, a few unsolved new research topics are proposed based on the framework. We follow the framework of NISs and propose certain rules and possible rules based on possible world semantics. Although each rule Ï„ depends on a large number of possible tables, we prove that each rule Ï„ is determined by examining only two Ï„ -dependent possible tables. The NIS-Apriori algorithm is an adjusted Apriori algorithm that can handle such tables. Furthermore, it is logically sound and complete with regard to the rules. Subsequently, the implementation of the NIS-Apriori algorithm in SQL is described and a few new topics induced by effects of NIS-Apriori-based rule generation are confirmed. One of the topics that are considered is the possibility of estimating missing values via the obtained certain rules. The proposed methodology and the environment yielded by NIS-Apriori-based rule generation in SQL are useful for table data analysis with three-way decisions

    Rough Set-Based Information Dilution by Non-deterministic Information

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    We have investigated rough set-based concepts for a given Non-deterministic Information System (NIS). In this paper, we consider generating a NIS from a Deterministic Information System (DIS) intentionally. A NIS Φ is seen as a diluted DIS ϕ, and we can hide the actual values in ϕ by using Φ. We name this way of hiding Information Dilution by non-deterministic information. This paper considers information dilution and its application to hiding the actual values in a table.14th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, RSFDGrC 2013, October 11-14, 2013, Halifax, NS, Canad
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