scholarly journals Evolving Lattices for Analyzing Behavioral Dynamics of Characters in Literary Text

2011 ◽  
Vol 9 (2) ◽  
pp. 502-509
Author(s):  
Eugene S Kitamura ◽  
Yukio-Pegio Gunji
Keyword(s):  
Rough Set ◽  
Text Analysis ◽  
Rough Set Theory ◽  
Window Size ◽  
Literary Text ◽  
Equivalence Relations ◽  
Behavior Dynamics ◽  
Behavioral Dynamics ◽  
The Relationship ◽  
Double Approximation

This paper is about an application of rough set derived lattices in order to analyze the dynamics of literary text. Due to the double approximation nature of rough set theory, a pseudo-closure obtained from two different equivalence relations allows us to form arbitrary lattices. Moreover, such double approximations with different equivalence relations permit us to obtain lattice fixed points based on two interpretations. The two interpretations used for literary text analysis are subjects and their attributes. The attributes chosen for this application are verbs. The progression of a story is defined by the sequence of verbs (or event occurrences). By fixing a window size and sliding the window down the story steps, we obtain a lattice representing the relationship between subjects and their attributes within that window frame. The resulting lattice provides information such as complementarity (lattice complement existence rate) and distributivity (lattice complement possession rate). These measurements depend on the overlap and the lack of overlap among the attributes of characters. As the story develops and new character and attributes are provided as the source of lattices, one can observe its evolution. In fact, a dramatic change in the behavior dynamics in a scene is reflected in the particular shifts in the character-attribute relationship. This method lets us quantify the developments of character behavioral dynamics in a story.

scholarly journals Evolving Lattices for Analyzing Behavioral Dynamics of Characters in Literary Text

2011 ◽  
Vol 9 (2) ◽  
pp. 502-509
Author(s):  
Eugene S Kitamura ◽  
Yukio-Pegio Gunji
Keyword(s):  
Rough Set ◽  
Text Analysis ◽  
Rough Set Theory ◽  
Window Size ◽  
Literary Text ◽  
Equivalence Relations ◽  
Behavior Dynamics ◽  
Behavioral Dynamics ◽  
The Relationship ◽  
Double Approximation

This paper is about an application of rough set derived lattices in order to analyze the dynamics of literary text. Due to the double approximation nature of rough set theory, a pseudo-closure obtained from two different equivalence relations allows us to form arbitrary lattices. Moreover, such double approximations with different equivalence relations permit us to obtain lattice fixed points based on two interpretations. The two interpretations used for literary text analysis are subjects and their attributes. The attributes chosen for this application are verbs. The progression of a story is defined by the sequence of verbs (or event occurrences). By fixing a window size and sliding the window down the story steps, we obtain a lattice representing the relationship between subjects and their attributes within that window frame. The resulting lattice provides information such as complementarity (lattice complement existence rate) and distributivity (lattice complement possession rate). These measurements depend on the overlap and the lack of overlap among the attributes of characters. As the story develops and new character and attributes are provided as the source of lattices, one can observe its evolution. In fact, a dramatic change in the behavior dynamics in a scene is reflected in the particular shifts in the character-attribute relationship. This method lets us quantify the developments of character behavioral dynamics in a story.

scholarly journals Using Rough Set to Construct the Enterprise Information Management System

2012 ◽  
Vol 6-7 ◽  
pp. 641-646
Author(s):  
Xing Wei
Keyword(s):  
Information Management ◽  
Set Theory ◽  
Rough Set ◽  
Management System ◽  
Rough Set Theory ◽  
Information Management System ◽  
Equivalence Relations ◽  
Knowledge Representation And Reasoning ◽  
Enterprise Information ◽  
Cpu Time

Rough set theory as a new hotspot in the field of artificial intelligence, it can effectively deal with incomplete and uncertain knowledge representation and reasoning. Rough set theory is built on the basis of the classification mechanism, it will be classified understand in a particular space on the equivalence relation, equivalence relations constitute the division of space. The paper puts forward using rough set to construct the enterprise information management system. The experiment shows the CPU Time in the attribute numbers, indicating that Jelonek is superior to rough set in building enterprise information management system.

An Application of Rough Set Analysis toa Psycho-Physiological Study - Assessing the RelationBetween Psychological Scale and Immunological Biomarker

2009 ◽  
Vol 13 (4) ◽  
pp. 352-359
Author(s):  
Shusaku Nomura ◽  
◽  
Yasuo Kudo ◽  
Keyword(s):  
Rough Set ◽  
Linear Correlation ◽  
Mental Stress ◽  
Rough Set Theory ◽  
Physiological State ◽  
Mental States ◽  
Psychological State ◽  
Analysis Method ◽  
Psychological Scale ◽  
The Relationship

This study aims at an application of rough set theory to illustrate the relationship between human psychological and physiological states. Recent behavioral medicine studies have revealed that various human secretory substances change according to mental states. These substances, the hormones and immune substances, show temporal increase against mental stress. Thus, it is frequently introduced as biomarkers of mental stress. The relationship between these biomarkers and human chronic stresses or daily mental states was also suggested in the previous studies. However the results of these studies were sometimes inconsistent with each other. Some technical reasons were indicated for this discrepancy. Among that, we focused on the analysis technique investigating the relationship between human psychological state, i.e., scores of a psychological scale, and physiological state, i.e., level of the secretory biomarkers. In this paper, we introduced Rough Set analysis method instead of using a conventional linear correlation analysis method. In the experiment, the salivary secretory immunoglobulin A (IgA), which is a major stress biomarker, of 20 male students was assessed before and after a short-term stressful mental workload. Also, 65 items of psychological mood scale was assessed as a psychological index. The result showed that some items strongly related with the change in the IgA, while no significant linear correlation was obtained among them.

Using Rough Set to Develop the Building Materials Management System

2013 ◽  
Vol 281 ◽  
pp. 658-663
Author(s):  
Jian Xu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Management System ◽  
Building Materials ◽  
Rough Set Theory ◽  
Equivalence Relations ◽  
Materials Management ◽  
Cpu Time ◽  
Materials Used

Materials used in buildings are collectively referred to building materials. Building materials can be divided into structural materials, decoration materials and some special materials. Rough set theory is built on the basis of the classification mechanism, it will be classified understand in a particular space on the equivalence relation, equivalence relations constitute the division of space. The paper puts forward using rough set to develop the building materials management system. The experiment shows the CPU Time in the attribute numbers, indicating that rough set is superior to FCA in building materials management system.

scholarly journals Topological properties of a pair of relation-based approximation operators

Filomat ◽  
2017 ◽  
Vol 31 (19) ◽  
pp. 6175-6183
Author(s):  
Yan-Lan Zhang ◽  
Chang-Qing Li
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Equivalence Relations ◽  
Topological Properties ◽  
Upper Approximation ◽  
Approximation Operators ◽  
Rough Approximation ◽  
Basic Concepts

Rough set theory is an important tool for data mining. Lower and upper approximation operators are two important basic concepts in the rough set theory. The classical Pawlak rough approximation operators are based on equivalence relations and have been extended to relation-based generalized rough approximation operators. This paper presents topological properties of a pair of relation-based generalized rough approximation operators. A topology is induced by the pair of generalized rough approximation operators from an inverse serial relation. Then, connectedness, countability, separation property and Lindel?f property of the topological space are discussed. The results are not only beneficial to obtain more properties of the pair of approximation operators, but also have theoretical and actual significance to general topology.

Approximations in Rough Sets vs Granular Computing for Coverings

2012 ◽  
pp. 152-163
Author(s):  
Guilong Liu ◽  
William Zhu
Keyword(s):  
Knowledge Discovery ◽  
Binary Relation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Knowledge Discovery In Databases ◽  
Equivalence Relations ◽  
Binary Relations ◽  
Covering Rough Set

Rough set theory is an important technique in knowledge discovery in databases. Classical rough set theory proposed by Pawlak is based on equivalence relations, but many interesting and meaningful extensions have been made based on binary relations and coverings, respectively. This paper makes a comparison between covering rough sets and rough sets based on binary relations. This paper also focuses on the authors’ study of the condition under which the covering rough set can be generated by a binary relation and the binary relation based rough set can be generated by a covering.

Rough set models of interval rough number information system

2021 ◽  
Vol 40 (1) ◽  
pp. 1655-1666
Author(s):  
Linhai Cheng ◽  
Yu Zhang ◽  
Yingying He ◽  
Yuejin Lv
Keyword(s):  
Information System ◽  
Rough Set ◽  
Rough Set Theory ◽  
Equivalence Relations ◽  
Uncertain Information ◽  
Approximation Accuracy ◽  
Similarity Degree ◽  
Application Range ◽  
Similar Relation ◽  
Degree Of Certainty

Classical rough set theory (RST) is based on equivalence relations, and does not have an effective mechanism when the attribute value of the objects is uncertain information. However, the information in actual problems is often uncertain, and an accurate or too vague description of the information can no longer fully meet the actual needs. Interval rough number (IRN) can reflect a certain degree of certainty in the uncertainty of the data when describing the uncertainty of the data, and can enable decision makers to make decisions more in line with actual needs according to their risk preferences. However, the current research on rough set models (RSMs) whose attribute values are interval rough numbers is still very scarce, and they cannot analyze the interval rough number information system (IRNIS) from the perspective of similar relation. therefore, three new interval rough number rough set models (IRNRSMs) based on similar relation are proposed in this paper. Firstly, aiming at the limitations of the existing interval similarity degree (ISD), new interval similarity degree and interval rough number similarity degree (IRNSD) are proposed, and their properties are discussed. Secondly, in the IRNIS, based on the newly proposed IRNSD, three IRNRSMs based on similar class, β-maximal consistent class and β-equivalent class are proposed, and their properties are discussed. And then, the relationships between these three IRNRSMs and those between their corresponding approximation accuracies are researched. Finally, it can be found that the IRNRSM based on the β-equivalent classes has the highest approximation accuracy. Proposing new IRNRSMs based on similar relation is a meaningful contribution to extending the application range of RST.

scholarly journals Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients

2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Aboul ella Hassanien ◽  
Mohamed E. Abdelhafez ◽  
Hala S. Own
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Confusion Matrix ◽  
Decision Rules ◽  
Minimal Subset ◽  
Discretization Algorithm ◽  
Diabetic Children ◽  
The Relationship ◽  
Accuracy Rates

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

scholarly journals A COMPARISON BETWEEN MRA AND ROUGH SET THEORY FOR MASS APPRAISAL. A CASE IN BARI

2004 ◽  
Vol 8 (4) ◽  
pp. 205-217 ◽  
Author(s):  
Maurizio D'amato
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Property Market ◽  
Specific Group ◽  
Market Data ◽  
Property Valuation ◽  
A Value ◽  
Valuation Methodology ◽  
The Relationship

Rough Set Theory is a property valuation methodology recently applied to property market data (d'Amato, 2002). This methodology may be applied in property market where few market data are available or where econometric analysis may be difficult or unreliable. This methodology was introduced by a polish mathematician (Pawlak, 1982). The model permit to estimate a property without defining an econometric model, although do not give any estimation of marginal or hedonic prices. I : ,he first version of RST was necessary to organize the data in classes before the valuation .The relationship between these classes defined if‐then rules. If a property belongs to a specific group then it will belong to a class of value. The relationship between the property and the class of value is dichotomous. In this paper will be offered a second version that improve the RST with a “value tolerance relation” in order to make more flexible the rule. In this case the results will come out from an explicit and specific relationship. The methodology has been tested on 69 transactions in the zone of Carrassi-Poggiofranco in the residential property market of Bari.

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