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

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 Parallelization of the NIS-apriori Algorithm for Data Mining

We have been developing the getRNIA software tool for data mining under uncertain information. The getRNIA software tool is powered by the NIS-Apriori algorithm, which is a variation of the well-known Apriori algorithm. This paper considers the parallelization of the NIS-Apriori algorithm, and implements a part of this algorithm based on the Apache-Spark environment. We especially apply the implemented software to two data sets, the Mammographic data set and the Mushroom data set in order to show the property of the parallelization. Even though this parallelization was not so effective for the Mammographic data set, it was much more effective for the Mushroom data set.19th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, September 7-9, 2015, Singapor

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

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

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