Observational Constraint to the Minimum Flux Corona Model

The minimum flux corona model proposed by Hearn was compared with the observational data. The temperature derived from Hearn\u27s model is lower than that derived from X-ray observation for latetype main-sequence stars. In the solar coronal hole, Hearn\u27s minimum condition is not realized. The minimum flux corona model seems to be adequate only to the solar quiet region

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

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

On NIS-Apriori Based Data Mining in SQL

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

Rules and Apriori Algorithm in Non-deterministic Information Systems

This paper presents a framework of rule generation in Non-deterministic Information Systems (NISs), which follows rough sets based rule generation in Deterministic Information Systems (DISs). Our previous work about NISs coped with certain rules, minimal certain rules and possible rules. These rules are characterized by the concept of consistency. This paper relates possible rules to rules by the criteria support and accuracy in NISs. On the basis of the information incompleteness in NISs, it is possible to define new criteria, i.e., minimum support, maximum support, minimum accuracy and maximum accuracy. Then, two strategies of rule generation are proposed based on these criteria. The first strategy is Lower Approximation strategy, which defines rule generation under the worst condition. The second strategy is Upper Approximation strategy, which defines rule generation under the best condition. To implement these strategies, we extend Apriori algorithm in DISs to Apriori algorithm in NISs. A prototype system is implemented, and this system is applied to some data sets with incomplete information

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

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

Granules for Association Rules and Decision Support in the getRNIA System

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

Granular Reasoning Using Zooming In & Out

n/

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

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 τ depends on a large number of possible tables, we prove that each rule τ is determined by examining only two τ -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

Families of the Granules for Association Rules and Their Properties

We employed the granule (or the equivalence class) defined by a descriptor in tables, and investigated rough set-based rule generation. In this paper, we consider the new granules defined by an implication, and propose a family of the granules defined by an implication in a table with exact data. Each family consists of the four granules, and we show that three criterion values, support, accuracy, and coverage, can easily be obtained by using the four granules. Then, we extend this framework to tables with non-deterministic data. In this case, each family consists of the nine granules, and the minimum and the maximum values of three criteria are also obtained by using the nine granules. We prove that there is a table causing support and accuracy the minimum, and generally there is no table causing support, accuracy, and coverage the minimum. Finally, we consider the application of these properties to Apriori-based rule generation from uncertain data. These properties will make Apriori-based rule generation more effective.10th International Conference, RSKT 2015, Held as Part of the International Joint Conference on Rough Sets, IJCRS 2015, November 20-23, 2015, Tianjin, Chin

A Proposal of Machine Learning by Rule Generation from Tables with Non-deterministic Information and Its Prototype System

A logical framework on Machine Learning by Rule Generation (MLRG) from tables with non-deterministic information is proposed, and its prototype system in SQL is implemented. In MLRG, the certain rules defined in Rough Non-deterministic Information Analysis (RNIA) are obtained at first, and each uncertain attribute value is estimated so as to cause the certain rules as many as possible, because the certain rules show us the most reliable information. This strategy is similar to the maximum likelihood estimation in statistics. By repeating this process, a standard table and the rules in its table are learned (or estimated) from a given table with non-deterministic information. Even though it will be hard to know the actual unknown values, MLRG will give a plausible estimation value.International Joint Conference on Rough Sets (IJCRS 2017), 3-7 July, 2017, Olsztyn, Polan