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

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

On Two Apriori-Based Rule Generators: Apriori in Prolog and Apriori in SQL

This paper focuses on two Apriori-based rule generators. The first is the rule generator in Prolog and C, and the second is the one in SQL. They are named Apriori in Prolog and Apriori in SQL, respectively. Each rule generator is based on the Apriori algorithm. However, each rule generator has its own properties. Apriori in Prolog employs the equivalence classes defined by table data sets and follows the framework of rough sets. On the other hand, Apriori in SQL employs a search for rule generation and does not make use of equivalence classes. This paper clarifies the properties of these two rule generators and considers effective applications of each to existing data sets

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