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

肺における散布性粒状病変の分布パターン : 気道散布性病変と血行散布性病変のX線病理学的研究

The distribution pattern of disseminated small lung nodules was radiologically stud- ied with special reference to the relationship between lesions and the bronchial branching system using inflated and fixed lungs of 6 cases Obtained from autopsy. The bronchogenous spread lesions, which were seen in endobronchial tuberculosis and bronchopneumonia, were located in the centriacinar portion of the secondary lobule. Their distribution patterns were regular and corresponded to the bronchial branching pattern. On the other hand, the hematogenous spread lesions, which were seen in miliary tuberculosis and pulmonary metastasis of carcinoma, had no relation to airway structures. They were randomly distributed regardless of bronchial branching. Recognition of these different distribution patterns is important for evaluating diffuse lung diseases by computed tomography

An Application of Rough Non-deterministic Information Analysis to Class Evaluation Data by Students

Non-deterministic Information Systems (NISs) have been recognized to be the most important framework for handling information incompleteness in tables, and several theoretical work has been examined. We follow this robust framework, and we have been developing algorithms and tool programs, which can handle the rough sets based concepts in NISs, on computers. We are simply calling this work Rough Non-deterministic Information Analysis (RNIA). This paper briefly surveys RNIA, and applies RNIA to class evaluation data, which consists of evaluation on 16 questions by 60 students. In this data, Question 16“(Q16) Rate this class in either 1, 2, 3, 4 or 5 grade”is the decision attribute, and data dependency and rules in the form of“Condition=> [Q16, 5]”are considered. The most characteristic point is that [Q16, 5] strongly depended upon question“(Q3) Was the teacher polite in every class?”. Question (Q3) is related to the emotional impression of a teacher, and the evaluation of a half-year class by every student seems to express every student\u27s emotional impression of a teacher

Niche separation of the nitrifying archaea and bacteria from sea surface to the trench bottom found in the Challenger Deep

http://www.godac.jamstec.go.jp/darwin/cruise/kairei/kr08-04/