A new approach to mining fuzzy databases using nearest neighbor classification by exploiting attribute hierarchies

Data classification is a well‐organized operation in the field of data mining. This article presents an application of the k‐nearest neighbor classification technique for mining a fuzzy database. We consider a data set in which attribute values have certain similarities in nature and analyze the observations for the domain of each attribute, on the basis of fuzzy similarity relations. The proposed technique is general and the presented case study demonstrates the suitability of using this fuzzy approach for mining fuzzy databases, especially when the database contains various levels of abstraction. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1277–1290, 2004.     

Updating extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: one is that attribute values are possibility distributions and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this article, we focus on such fuzzy relational databases and investigate three update operations for the fuzzy relational databases, which are Insertion, Deletion, and Modification, respectively. We develop the strategies and implementation algorithms of these operations. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 237–258, 2007.     

Reduction of the decision table: A rough approach

In this article, we generalize Pawlak's rough approach for simplifying the decision table in an information system. We consider an information system where attribute values are not always quantitative, but are rather subjective, having vague or imprecise meanings. Some objects may have attribute values that are almost identical; that is, they cannot be distinguished clearly by the attributes. This observation is analyzed here being based on fuzzy proximity relations on different domain of attributes. Finally we find out the minimal solution of the table. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1143–1150, 2004.     

基于规则面向属性的数据库归纳的无回溯算法

该文提出了基于规则的面向属性知识发现方法的无回溯算法.把背景知识理解为特殊的逻辑程序,并把它的子句展开为完全归结子句,然后按照用户要求,定义并确定每个属性的恰当层次.每个属性的多个值归纳为恰当层次中的值,只需一遍扫描,因此无需回溯.     

Normalization and lossless join decomposition of similarity‐based fuzzy relational databases

Fuzzy relational database models generalize the classical relational database model by allowing uncertain and imprecise information to be represented and manipulated. In this article, we introduce fuzzy extensions of the normal forms for the similarity‐based fuzzy relational database model. Within this framework of fuzzy data representation, similarity, conformance of tuples, the concept of fuzzy functional dependencies, and partial fuzzy functional dependencies are utilized to define the fuzzy key notion, transitive closures, and the fuzzy normal forms. Algorithms for dependency preserving and lossless join decompositions of fuzzy relations are also given. We include examples to show how normalization, dependency preserving, and lossless join decomposition based on the fuzzy functional dependencies of fuzzy relation are done and applied to some real‐life applications. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 885–917, 2004.     

Design and implementation of index structures for fuzzy spatial databases

Over the years database management systems have evolved to include spatially referenced data. Because spatial data are complex and have a number of unique constraints (i.e., spatial components and uncertain properties), spatial database systems can be effective only if the spatial data are properly handled at the physical level. Therefore, it is important to develop an effective spatial and aspatial indexing technique to facilitate flexible spatial and/or aspatial querying for such databases. For this purpose we introduce an indexing approach to use (fuzzy) spatial and (fuzzy) aspatial data. We use a number of spatial index structures, such as Multilevel Grid File (MLGF), G-tree, R-tree, and R^*-tree, for fuzzy spatial databases and compare the performances of these structures for various flexible queries. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 805–826, 2007.     

一种基于信息熵的模糊粗糙集知识获取方法

模糊粗糙集融合了模糊集和粗糙集的思想,是一种新的处理模糊和不确定性知识的软计算工具。针对属性为模糊值的信息系统,提出了一种基于熵的模糊粗糙集知识获取方法:首先通过模糊相似度量计算出各属性下对象的模糊相似值,再根据模糊相似关系构造模糊等价关系,然后根据模糊等价关系建立属性集的信息熵表示,继而使用基于信息熵的决策表属性约简算法获取规则。最后,通过一个实例,分析说明了这种算法的合理有效性。     

LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH

Knowledge discovery in databases, or dala mining, is an important direction in the development of data and knowledge-based systems. Because of the huge amount of data stored in large numbers of existing databases, and because the amount of data generated in electronic forms is growing rapidly, it is necessary to develop efficient methods to extract knowledge from databases. An attribute-oriented rough set approach has been developed for knowledge discovery in databases. The method integrates machine-learning paradigm, especially learning-from-examples techniques, with rough set techniques. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause-effect relationship among the attributes in the database is analyzed using rough set techniques, and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute-oriented induction combined with rough set theory provide an efficient and effective mechanism for knowledge discovery in database systems.     

GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting

The aim of this paper is to investigate the multiple attribute decision-making problems with intuitionistic fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method, by which the attribute weights can be determined. Then, based on the traditional GRA method, calculation steps for solving intuitionistic fuzzy multiple attribute decision-making problems with incompletely known weight information are given. The degree of grey relation between every alternative and positive-ideal solution and negative-ideal solution are calculated. Then, a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of grey relation to both the positive-ideal solution (PIS) and negative-ideal solution (NIS) simultaneously. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

基于模糊知识粒度的混合属性约简算法

现实世界中常常包含着海量的、不完整的、模糊及不精确的数据或对象,使得模糊信息粒化成为近年来研究趋势。利用论域上的模糊等价关系定义了模糊粒度世界的模糊知识粒度,给出了新的属性约简条件和核属性计算方法,以便更好地挖掘出潜在的、有利用价值的信息。针对粗糙集在对连续属性约简的过程中容易造成信息缺失和不能对模糊属性处理的现象,提出了一种基于模糊知识粒度对混合决策系统约简的启发式算法,省去了连续属性离散化过程,减少了计算量,为离散值域和混合值域约简提供了统一的方法。最后通过实例验证了其有效性。     

基于数据流的视频解码微控制器设计

针对数字视频解码芯片设计中多种视频协议的解析问题,提出一种专用微控制器设计方法。该方法采用面向视频解析的指令集,针对视频解析过程的特点对指令进行特别优化,采用配合该专用微控制器的视频解析模型,较好实现了MPEG1/2、AVS、H.264等视频协议的兼容,保证了解码效率且不会增加芯片面积和功耗。     

优势关系下属性值粗化细化时近似集分析

基于优势关系粗糙集模型反映属性间的偏好情况,实际上多数数据库中的数据是动态变化的。如何利用已有的信息更新近似集对于提高知识发现效率有重要意义。提出不完备信息系统在优势关系下属性值粗化细化的定义,讨论优势关系下不完备信息系统中属性值粗化细化时近似集的变化情况,对比分析优势关系下属性值粗化细化前后的粗糙近似精度和粗糙近似质量。通过实例分析验证了该方法的有效性。     

Flexible querying via if-sets

The traditional query languages used in database management systems require precise and unambiguous queries only. Fuzzy querying was introduced to relax this rigidity and allow the user more natural information retrieval. In this article we suggest how to enrich fuzzy querying by the use of IF-sets. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 587–597, 2007.     

一种有效的估计关系数据库中空值的方法

由于客观世界的复杂性,信息缺失、不确定信息是普遍存在的。数据库作为表达现实世界的一种工具,使用空值来表达信息缺失的问题。针对关系数据库中的空值问题,提出一种基于模糊聚类和线性回归的空值估计方法。该方法首先对数据表中的数据进行挖掘,找出与被估计属性相关联的属性集。该过程仅利用数据本身提供的信息,避免了由专家决定条件属性时由于主观性造成的误差。其次根据所得属性集进行模糊聚类得到对原始数据的一个划分,再基于所得分簇和线性回归给出一个估计关系表中空值的方法。最后利用平均绝对错误率来衡量算法估值的准确率。实验结果表明该方法估值的结果与其他方法相比具有较高的准确率。     

基于面向属性泛化及信息增益的数据挖掘方法研究

针对面向属性的归纳方法及粗糙集方法对知识粒性连续性的特点，将两者有机结合，利用面向属性归纳方法对数据进行泛化，再用属性的信息增益技术寻找泛化属性之间的数据依赖关系，能快速地在数据集中挖掘分类规则。将其应用于经典的仿真算例中，仿真结果合理、可靠。     

Topological solution of missing attribute values problem in incomplete information tables

In this paper, we present a new method of data decomposition to avoid the necessity of reasoning from data with missing attribute values. We define firstly a general binary relation on the original incomplete dataset. This binary relation generates data subsets without missing values. These data subsets are used to generate a topological base relation which decomposes datasets. We investigate a new approach to find the missing values in incomplete datasets. New pre-topological approximations are initiated and some of their properties are proved. Also, pre-topological measures are defined and studied. Finally, the reducts and the core of incomplete information system are determined.     

Clustering web search results using fuzzy ants

Algorithms for clustering Web search results have to be efficient and robust. Furthermore they must be able to cluster a data set without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behavior of real ants generally meet these requirements. In this article we propose a novel approach to ant‐based clustering, based on fuzzy logic. We show that it improves existing approaches and illustrates how our algorithm can be applied to the problem of Web search results clustering. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 455–474, 2007.     

Entropy measures and granularity measures for interval and set-valued information systems

In many practical situations, some of the attribute values for an object may be interval and set-valued. The interval and set-valued information systems have been introduced. According to the semantic relation of attribute values, interval and set-valued information systems can be classified into two categories, disjunctive (type 1) and conjunctive (type 2) systems. This paper mainly focuses on semantic interpretation of type 1. Then, a new fuzzy preference relation for interval and set-valued information systems is defined. Moreover, based on the new fuzzy preference relation, the concepts of fuzzy information entropy, fuzzy rough entropy, fuzzy knowledge granulation and fuzzy granularity measure are studied and relationships between entropy measures and granularity measures are investigated. Finally, an illustrative example to substantiate the theoretical arguments is given. These results may supply a further understanding of the essence of uncertainty in interval and set-valued information systems.     

Data-driven discovery of quantitative rules in relational databases

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases