Ordered Information Systems Research Articles

Knowledge acquisition of multi-granularity ordered information systems

Knowledge reduction is an essential issue in data mining. However, most of the existing studies consider the problem of knowledge reduction in a single-scale system. When the system expands to a multi-scale information system (MIS), the existing methods are not available. To overcome this challenge, we design a universal framework to reduce attributes and select optimal scales (OSs) based on the dominance relation in ordered information systems (OISs). Firstly, we analyze the effect of scale on rough approximations and correlation measures in multi-scale ordered information systems (MOISs). Secondly, the relation between attribute reduction and OS selection is explored in OISs. Moreover, the corresponding algorithms are designed around both tasks. Due to our universal framework considering the decisions at the finest, optimal and coarsest scale, our study is an extension of the three-way decision. Thirdly, a novel MOIS construction algorithm is explored to transform an OIS into an MIS. Thus, the proposed reduction algorithms can be operated in any OIS. Experiments substantiate that models trained by data at the OS can improve their stability and the attribute reduction task is also suitable for the proposed algorithm.

A large-scale group decision-making method based on group-oriented rough dominance relation in scenic spot service improvement

In today’s age of big data and information, large-scale group decision-making has become an essential aspect of modern economy, science, and technology. This paper proposes a large-scale group decision-making method that leverages group-oriented rough dominance relation to identify the worst group when addressing complex issues that involve a large number of decision-makers. The proposed method entails building a set-valued ordered information system that utilizes clustering learning to reduce data dimensions and reduce the dimensions of decision space, thereby improving the efficiency of the decision-making process. Additionally, it proposes a novel group-oriented rough dominance relation based on dominance-based rough set theory. By clarifying this advantage relationship, more targeted focus is placed on the group that needs improvement, thereby improving decision-making effectiveness. The proposed method calculates the advantage degree of alternative group plans to select the worst group. The main purpose is to compare the advantages and disadvantages of different groups by defining a metric that quantifies the rough dominance relation between groups, thereby improving the objectivity and repeatability of the decision-making process. Finally, the study applies the proposed method to a case study aimed at improving various types of services in European scenic spots, and the benefits of the method are discussed. Experimental analysis shows that the method in this study can screen the groups that should be improved most in this case, showing the benefits and applicability of the proposed method, and providing valuable insights for complex decision-making problems involving multiple decision-makers.

A Novel Dynamic Fusion Approach Using Information Entropy for Interval-Valued Ordered Datasets

Information fusion is capable of fusing and transforming information originated from multiple sources into an integrated representation. As an important representative of information, interval-valued ordered data aims at characterizing inaccurate and ambiguous information. However, existing methods are inappropriate when applied to the multi-source fusion for them. In addition, it is inevitable that in daily-life the sources and attributes of multi-source information systems may update at the same time. Hence there is a need to fuse data as efficiently as possible. Inspired by these deficiencies, we pay attention to the effective and efficient fusion of multi-source interval-valued ordered data in varieties of cases. First, the concepts of fuzzy dominance relation and dominance classes based on it are put forward between any two samples of interval-valued ordered information systems. Second, we define the fuzzy dominating and dominated conditional entropy. Then the fusion model is established and it is multi-source interval-valued ordered data oriented. Furthermore, it is true that there are numerous real-life applications related to concurrent change of both sources and attributes. Consequently, we design four incremental mechanisms and algorithms for fusing multi-source interval-valued data on the ground of the static condition. Eventually, a series of experiments is carried out on twelve datasets to verify that the proposed fusion approach outperforms other comparative methods on efficacy. Meanwhile, our incremental fusion algorithms are efficient compared with the static one for updating multi-source interval-valued data when sources and attributes are in the simultaneous variation.

A novel rough set method based on adjustable-perspective dominance relations in intuitionistic fuzzy ordered decision tables

With the increasing diversification and complexity of information and data, it is crucial to monitor and process data and information from multiple perspectives. There is a general consensus that dominance-based rough set approaches are the most effective methods for ordered information system research. Regardless, limitations and irrationality still exist in dominance relations because they cannot reflect the different emphases of data under different feature sets, nor can they meet the requirements for describing data information in the real world. To enable the dominance relation rough set model to be more effectively applied to practical problems in line with human cognition, our work focuses on developing adjustable-perspective dominance relations by fusing three different dominance relations in an intuitionistic fuzzy ordered decision table. On this basis, we construct a single-perspective rough set (SPRS) model to realize knowledge mining and rule extraction from various perspectives. Additionally, we present two types of different-perspective rough sets (DPRS) that reduce the restriction of single-perspective evaluation data in realistic problems and discuss rule extraction. Additionally, we compare SPRS and DPRS to other dominance-based rough set models from the perspectives of the ordinal classification, roughness, and dependence degree. Finally, we analyze eight UCI datasets and present a series of comparative experiments to demonstrate the effectiveness and rationality of the proposed model.

Library fever: lesbian memoir and the sexual politics of order

ABSTRACT Scholars across disciplines have recognised the symbolic and practical importance of information infrastructures in queer lives – from archives to the contemporary Internet. This essay builds on this work to argue that crucial to understanding this fact is what it says about the place of structure and order in queer history and theory. While these ideas have tended to be maligned in queer thought, and imagined primarily as manifestations of Foucauldian power, the approach taken here is to highlight their specifically queer affordances. The essay focuses on a range of high-profile lesbian memoirs from the U.S. that prominently thematise the place of libraries and information access in lesbian lives: anthologies of coming out stories, Audre Lorde’s Zami and Alison Bechdel’s Fun Home. Libraries in these texts are lifelines, workplaces and romantic scenes: they become imbued with lesbian meaning. But they are also complexes of standardisation, order and classification. The texts’ attachments to ordered information systems and to specifically lesbian identities are at odds with queer thought’s long-standing aversions to both classification and identity. But this essay takes this tension as the occasion to interrogate core assumptions of queer studies that place queer lives only in opposition to all structure and order.

Update of approximations in ordered information systems under variations of attribute and object set

Many collected data from real world applications often evolve when new attributes or objects are inserted or old ones are removed. The set approximations of ordered information systems (OIS) need to be updated from time to time for further data reduction, analysis, or decision-making. Incremental approaches are feasible and efficient techniques for updating approaches when any variation occurs. In this paper, considering OIS for multi-criteria classification problems, we discuss the principles of incrementally updating approximations in dominance relation based method in four different types of dynamic environments which combine the changes of both attribute set and object set. In each dynamic environment, the corresponding updating principles and algorithm are given with detail proofs. The experimental results and analysis on UCI data sets show that the proposed incremental approach outperforms the non-incremental method and the integration of current incremental algorithms in the implementation efficiency.

Two Different Views for Generalized Rough Sets with Applications

Rough set philosophy is a significant methodology in the knowledge discovery of databases. In the present paper, we suggest new sorts of rough set approximations using a multi-knowledge base; that is, a family of the finite number of general binary relations via different methods. The proposed methods depend basically on a new neighborhood (called basic-neighborhood). Generalized rough approximations (so-called, basic-approximations) represent a generalization to Pawlak’s rough sets and some of their extensions as confirming in the present paper. We prove that the accuracy of the suggested approximations is the best. Many comparisons between these approaches and the previous methods are introduced. The main goal of the suggested techniques was to study the multi-information systems in order to extend the application field of rough set models. Thus, two important real-life applications are discussed to illustrate the importance of these methods. We applied the introduced approximations in a set-valued ordered information system in order to be accurate tools for decision-making. To illustrate our methods, we applied them to find the key foods that are healthy in nutrition modeling, as well as in the medical field to make a good decision regarding the heart attacks problem.

Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems

Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems

Multiple Granulation Rough Set Approach to Interval-Valued Intuitionistic Fuzzy Ordered Information Systems

As a further extension of the fuzzy set and the intuitive fuzzy set, the interval-valued intuitive fuzzy set (IIFS) is a more effective tool to deal with uncertain problems. However, the classical rough set is based on the equivalence relation, which do not apply to the IIFS. In this paper, we combine the IIFS with the ordered information system to obtain the interval-valued intuitive fuzzy ordered information system (IIFOIS). On this basis, three types of multiple granulation rough set models based on the dominance relation are established to effectively overcome the limitation mentioned above, which belongs to the interdisciplinary subject of information theory in mathematics and pattern recognition. First, for an IIFOIS, we put forward a multiple granulation rough set (MGRS) model from two completely symmetry positions, which are optimistic and pessimistic, respectively. Furthermore, we discuss the approximation representation and a few essential characteristics for the target concept, besides several significant rough measures about two kinds of MGRS symmetry models are discussed. Furthermore, a more general MGRS model named the generalized MGRS (GMGRS) model is proposed in an IIFOIS, and some important properties and rough measures are also investigated. Finally, the relationships and differences between the single granulation rough set and the three types of MGRS are discussed carefully by comparing the rough measures between them in an IIFOIS. In order to better utilize the theory to realistic problems, an actual case shows the methods of MGRS models in an IIFOIS is given in this paper.

A data fusion method in wireless sensor network based on belief structure

Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

Attribute reduction based on improving DIT in interval‐valued ordered information system

Attribute reduction based on improving DIT in interval‐valued ordered information system

Double-quantitative variable consistency dominance-based rough set approach

Rough set model with double quantification satisfies the requirement of quantitative information in practical applications, it has better fault tolerance than probabilistic rough set model considering only relative quantification and graded rough set model considering only absolute quantification. In this paper, two kinds of consistency levels are introduced from the perspective of double quantification in an ordered information system, namely relative quantitative consistency level and absolute quantitative consistency level. The single-quantitative variable consistency dominance-based rough set models based on these two kinds of quantitative consistency levels and their basic properties with the relevant three-way decision rules are discussed respectively in an ordered information system. Moreover, two kinds of double-quantitative variable consistency dominance-based rough set models and their basic properties with the relevant decision rules based on these two kinds of quantitative consistency levels are introduced. A consistency analysis of decision making in a practical case study is used to illustrate and interpret the double-quantitative variable consistency rough set models and the related decision rules in the ordered information system. The obvious shortcomings of dominance-based rough set approach (DRSA) without quantitative information are compared to explain the advantages of the quantitative variable consistency dominance-based rough sets with the two consistency levels in the practical case study.

Dynamic dominance-based multigranulation rough sets approaches with evolving ordered data

In practical applications, there exist lots of ordered information systems (OISs). In the process of dealing with OISs, dominant preference, which plays a significant role in decision making, should be taken into consideration. With the increasing of data capacity, OISs often evolve with time. In order to extract updated knowledge from evolving ordered data, we have to elaborate computation efforts to re-calculate entire data, which consumes a significant computational cost. Therefore, the computational efficiency is extremely low. In response to this challenge, matrix-based dynamic dominance-based multigranulation rough sets (DMGRSs) approaches, which can improve computational efficiency for updating knowledge, are explored to update multigranulation approximations in dynamic ordered information systems with evolving data. To begin with, we present a matrix representation of dominance-based multigranulation approximations according to the dominant relation matrix and relevant column vectors of each granular structure. Afterwards, the incremental strategies to update dominance-based multigranulation approximations in OISs are proposed when adding or deleting objects. Furthermore, the corresponding dynamic algorithms, which avoid some unnecessary calculations, are explored in DMGRSs. Finally, extensive experiments carried out on nine UCI data sets indicate that the explored dynamic algorithms can achieve promising performance.

Information Structures in an Ordered Information System Under Granular Computing View and Their Optimal Selection Based on Uncertainty Measures

Information structures (i-structures) in an ordered information system (OIS) are mathematical structures of the information granules (i-granules) granulated from the data set of this OIS. This article investigates i-structures in an OIS with granular computing (GrC) view, i.e., i-structures in an OIS are seen as granular structures. Dependence and independence between i-structures are first depicted in the same OIS. Then, information distance (i-distance) between i-structures in the same OIS are proposed, and information entropy in an OIS can be expressed by i-distance between i-structures in this OIS is proved. Next, properties of i-structures in an OIS are shown. Moreover, group, lattice and map characters of i-structures in an OIS are received, and some invariant characters of i-structures in an OIS under homomorphisms are obtained. Finally, the optimal selection of i-structures in an OIS based on the proposed measures are studied. These results will contribute to build a framework of GrC in an OIS.

A knowledge acquisition method based on concept lattice and inclusion degree for ordered information systems

In some information system with order features, when users consider “greater than” or “less than” relations to a certain degree rather than in the full sense, using traditional methods may face great limitations. In light of natural connections among concept lattice, inclusion degree, order relations, and the feasibility of mutual integration among the three (concept lattice is essentially a type of data analysis tool using binary relations as research objects, while inclusion degree is a type of powerful tool for measuring uncertain order relations), the paper attempts to analyze uncertain order relations quantitatively within the framework of integration theory of concept lattice and inclusion degree. By which, the research scope of order relations undergoes an expansion-to-contraction process. Namely, certain order relations are first expanded to fuzzy or uncertain relations, and then the fuzzy or uncertain relations are allowed to contract to a degree of certainty by setting threshold parameters. Clearly, by properly widening the research scope of order relations, the model not only has good robustness and generalization ability, but also can meet actual needs flexibly. On this basis, solutions for algebraic structure, reduction, core, dependency, et al. are further studied deeply in ordered information systems. In short, the paper, as a meaningful try and exploration, is conducive to the integration of theories, and may offer some new and feasible ways for the study of order relations and ordered information systems.

A novel approach for efficient updating approximations in dynamic ordered information systems

Dynamic data in real-time application are typically updating in a multi-dimensional manner. In this paper, we introduce a novel approach based on Dominance-based Rough Set Approach (DRSA) to efficiently deal with the multi-dimensional variations of attribute set and attribute values in dynamic Ordered Information Systems (OIS). We improve the original notion of the P-generalized decision domains to make the feature value matrix be dominance symmetrical, and propose an efficient strategy based on the improved notion to obtain the dominance feature matrix. Then, we employ the dominance-feature-matrix-based incremental strategy to avoid repeated comparisons between original attributes, so that to efficiently update rough approximations of DRSA with the simultaneously increased attribute set and varied attribute values. In our approach, the steps based on these two combined strategies can work altogether or separately, not only efficiently dealing with the simultaneously increased attribute set and varied attribute values, but also efficiently dealing with the individually increased attribute set or varied attribute values in dynamic OIS. Efficient algorithm based on the updating strategies is designed and multiple groups of experiments are conducted. Experimental results on different real-world data sets show that the proposed algorithm is much faster than other algorithms for dealing with the multi-dimensional or the single-dimensional variations of attribute set and attribute values.

Local multigranulation decision-theoretic rough set in ordered information systems

As a generalized extension of Pawlak’s rough set model, the multigranulation decision-theoretic rough set model in ordered information systems utilizes the basic set assignment function to construct probability measure spaces through dominance relations. It is an effective tool to deal with uncertain problems and widely used in practical decision problems. However, when the scale of dataset is large, it takes a lot of time to characterize the approximations of the target concept, as well as complicated calculation processes. In this paper, we develop a novel model called local multigranulation decision-theoretic rough set in an ordered information system to overcome the above-mentioned limitation. Firstly, to reduce the computing time of the information granule independent of the target concept, we only use the characterization of the elements in the target concept to approximate this target concept. Moreover, the corresponding local multigranulation decision-theoretic rough set in an ordered information system is addressed according to the established local model, and the comparisons are made between the proposed local algorithm and the algorithm of original multigranulation decision-theoretic rough set in ordered information systems. Finally, the validity of the local approximation operators is verified through the experimental evaluation using six datasets coming from the University of California-Irvine (UCI) repository.

Domain-wise approaches for updating approximations with multi-dimensional variation of ordered information systems

Dominance-based Rough Set Approach (DRSA) is widely applied in processing the information and data with a preference-order relation. In many real-life Ordered Information Systems (OIS), the attribute values and objects may vary simultaneously with time. Existing incremental updating algorithms in OIS are all single-dimensional, i.e., they only consider the individual variation of attribute values or objects. In addition, all the objects need to be compared with each other during updating. In this paper, we focus on designing specific multi-dimensional algorithms to efficiently update approximations on the simultaneous variation of attribute values and objects in OIS. The properties of the P-generalized decision in DRSA are firstly presented. Then two novel notions e.g., the P-generalized decision lower and upper domains, are defined, respectively. These two notions indicate the practical dominance dependence between different objects in OIS, and provide a simplified definition of the P-generalized decision. The approach based on these two notions can greatly reduce the comparisons between different objects, as well as provide novel strategies which can efficiently obtain the updated P-generalized decision to obtain the updated approximations. Furthermore, we develop two domain-wise algorithms which correspond to two cases of the multi-dimensional variation of attribute values and objects in OIS, respectively. Moreover, in implementing the algorithms, the sorting strategies are integrated to solve the problem of time-consuming traversals. A series of experimental results illustrate that our proposed domain-wise algorithms are not only more efficient than both of the traditional static algorithm and the integrated single-dimensional algorithms for dealing with the multi-dimensional variation of attribute values and objects, but also more efficient than the single-dimensional algorithms for dealing with the individual variation of objects in OIS.

Attribute reduction in interval‐valued fuzzy ordered decision tables via evidence theory

There are two different theory methods that are rough set theory and evidence theory, but these two theories can both handle some incomplete and uncertain information. In this study, these two models are combined in the interval-valued fuzzy ordered information system (IVFOIS). Belief functions and plausibility functions are proposed based on dominance relations in IVFOISs. The belief and plausibility reducts are defined in interval-valued fuzzy ordered decision tables (IVFODTs) and the attribute reduction of IVFODTs based on evidence theory is established. Finally, the authors use an instance to verify the above argument.

Multi-Granulation Graded Rough Intuitionistic Fuzzy Sets Models Based on Dominance Relation

From the perspective of the degrees of classification error, we proposed graded rough intuitionistic fuzzy sets as the extension of classic rough intuitionistic fuzzy sets. Firstly, combining dominance relation of graded rough sets with dominance relation in intuitionistic fuzzy ordered information systems, we designed type-I dominance relation and type-II dominance relation. Type-I dominance relation reduces the errors caused by single theory and improves the precision of ordering. Type-II dominance relation decreases the limitation of ordering by single theory. After that, we proposed graded rough intuitionistic fuzzy sets based on type-I dominance relation and type-II dominance relation. Furthermore, from the viewpoint of multi-granulation, we further established multi-granulation graded rough intuitionistic fuzzy sets models based on type-I dominance relation and type-II dominance relation. Meanwhile, some properties of these models were discussed. Finally, the validity of these models was verified by an algorithm and some relative examples.