A NEW METHOD TO GENERATE FUZZY RULES FROM RELATIONAL DATABASE SYSTEMS FOR ESTIMATING NULL VALUES

Fuzzy decision trees can be used to generate fuzzy rules from training instances to deal with forecasting and classification problems. We propose a new method to construct fuzzy decision trees from relational database systems and to generate fuzzy rules from the constructed fuzzy decision trees for estimating null values, where the weights of attributes are used to derive the values of certainty factors of the generated fuzzy rules. We use the concept of "coefficient of determination" of the statistics to derive the weights of the attributes in relational database systems and use the normalized weights of the attributes to derive the values of certainty factors of the generated fuzzy rules. Furthermore, we also use regression equations of the statistics to construct a complete fuzzy decision tree for generating better fuzzy rules. The proposed method obtains a higher average estimated accuracy rate than the existing methods for estimating null values in relational database systems.     

A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values

In this paper, we present a new method to generate weighted fuzzy rules using genetic algorithms for estimating null values in relational database systems, where there are negative functional dependency relationships between attributes. The proposed method can get higher average estimated accuracy rates than the method presented in [Chen, S. M., & Huang, C. M. (2003). Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Transactions on Fuzzy Systems, 11(4), 495–506].     

Functional dependencies with null values, fuzzy values, and crispvalues

Based on the concepts of the semantic proximity, we present a definition of the fuzzy functional dependency, We show that the inference rules for fuzzy functional dependencies, which are the same as Armstrong's axioms for the crisp case, are correct and complete. We also show that dependent constraints with dull values constitute a lattice. Functional dependencies in classical relational databases and null functional dependencies can be viewed as a special case of fuzzy functional dependencies. By applying the unified functional dependencies to the relational database design, we can represent the data with fuzzy values, null values and crisp values under relational database management systems, By using fuzzy functional dependencies, we can compress the range of a fuzzy value and make this fuzzy value “clearer”     

Estimating null values in relational database systems using automatic clustering and multiple regression techniques

In this paper, we present a new method for estimating null values in relational database systems using automatic clustering and multiple regression techniques. First, we present a new automatic clustering algorithm for clustering numerical data. The proposed automatic clustering algorithm does not need to determine the number of clusters in advance and does not need to sort the data in the database in advance. Then, based on the proposed automatic clustering algorithm and multiple regression techniques, we present a new method to estimate null values in relational database systems. The proposed method estimating null values in relational database systems only needs to process a particular cluster instead of the whole database. It gets a higher average estimation accuracy rate than the existing methods for estimating null values in relational database systems.     

A new method to estimate null values in relational database systems based on automatic clustering techniques

In this paper, we present a new method for estimating null values in relational database systems based on automatic clustering techniques. The proposed method clusters data in advance, such that it only needs to process the most proper clusters instead of all the data in the relational database system for estimating null values. The average estimated accuracy rate of the proposed method is better than the existing methods for estimating null values in relational database systems.     

ESTIMATING NULL VALUES IN THE DISTRIBUTED RELATIONAL DATABASES ENVIRONMENT

To estimate nullvalues in relational database systems is an important research topic. In Chen and Yeh (1997) a method for estimating null values in relational database systems was presented. In Chen and Chen (1997) a method for fuzzy query translation for information in the distributed relational databases environment was presented. In this article, the works of Chen and Chen (1997) and Chen and Yeh (1997) are extended to propose a method for estimating null values in the distributed relational databases environment. The proposed method provides a useful way to estimate incomplete data when the relations stored in a failed server cannot be accessed in the distributed relational databases environment.     

A new approach for estimating null value in relational database

In general, a database system will not operate properly if it exist some null values of attributes in the system. In this paper, we propose a new approach to estimate null values in relational database, which utilize other clustering algorithm to cluster data, and use fuzzy correlation and distance similarity to calculate the correlation of different attribute. For verifying our method, this paper utilize mean of absolute error rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves importance than the existing methods for estimating null values in relational database systems.     

An efficient method for estimating null values in relational databases

Generally, a database system containing null value attributes will not operate properly. This study proposes an efficient and systematic approach for estimating null values in a relational database which utilizes clustering algorithms to cluster data, and a regression coefficient to determine the degree of influence between different attributes. Two databases are used to verify the proposed method: (1) Human resource database; and (2) Waugh's database. Furthermore, the mean of absolute error rate (MAER) and average error are used as evaluation criteria to compare the proposed method with other methods. It demonstrates that the proposed method is superior to existing methods for estimating null values in relational database systems. Jia-Wen Wang was born on September 5, 1978, in Taipei, Taiwan, Republic of China. She received the M.S. degree in information management from the National Yunlin University of Science and Technology, Yunlin, Taiwan, in 2003. Since 2003, she has been a PhD degree student in Information Management Department at the National Yunlin University of Science and Technology. Her current research interests include fuzzy systems, database systems, and artificial intelligence. Ching-Hsue Cheng received the B.S. degree in mathematics from Chinese Military Academy, Taiwan, in 1982, the M.S. degree in applied mathematics from the Chung Yuan Christian University, Taiwan, in 1988, and the Ph.D. degree in system engineering and management from National Defence University, Taiwan, in 1994. Currently, he is a professor of the Department of Information Management, National YunLin University of Technology & Science. His research interests are in decision science, soft computing, software reliability, performance evaluation, and fuzzy time series. He has published more than 120 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with government, and other research-sponsoring agencies.     

Fuzzy database query languages and their relational completenesstheorem

Two fuzzy database query languages are proposed. They are used to query fuzzy databases that are enhanced from relational databases in such a way that fuzzy sets are allowed in both attribute values and truth values. A fuzzy calculus query language is constructed based on the relational calculus, and a fuzzy algebra query language is also constructed based on the relational algebra. In addition, a fuzzy relational completeness theorem such that the languages have equivalent expressive power is proved     

Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms

In recent years, some methods have been proposed to estimate values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.     

A new method for computing fuzzy functional dependencies in relational database systems

In this paper, we present a new method for computing fuzzy functional dependencies between attributes in fuzzy relational database systems. The method is based on the use of fuzzy implications. A literature analysis has shown that there is no algorithm that would enable the identification of attribute relationships in fuzzy relational schemas. This fact was the motive for development a new methodology in the analysis of fuzzy functional dependencies over a given set of attributes. Solving this, not so new problem, is not only research challenge having theoretical importance, but it also has practical significance. Possible applications of the proposed methodology include GIS, data mining, information retrieval, reducing data redundancy in fuzzy relations through implementation of logical database model, estimation of missing values etc.     

Fuzzy query translation for relational database systems

The paper presents a new method for fuzzy query translation based on the alpha-cuts operations of fuzzy numbers. This proposed method allows the retrieval conditions of SQL queries to be described by fuzzy terms represented by fuzzy numbers. It emphasizes friendliness and flexibility for inexperienced users. The authors have implemented a fuzzy query translator to translate user's fuzzy queries into precise queries for relational database systems. Because the proposed method allows the user to construct his fuzzy queries intuitively and to choose different retrieval threshold values for fuzzy query translation, the existing relational database systems will be more friendly and more flexible to the users.     

A NEW METHOD FOR FUZZY QUERY PROCESSING IN RELATIONAL DATABASE SYSTEMS

In this paper, we present a new method for fuzzy query processing in relational database systems based on automatic clustering techniques and weighting concepts. The proposed method allows the query conditions and the weights of query items of users' fuzzy SQL queries to be described by linguistic terms represented by fuzzy numbers. Because the proposed fuzzy query processing method allows the users to construct their fuzzy queries more conveniently, the existing relational database systems will be more intelligent and more flexible to the users.     

Interval-valued fuzzy decision trees with optimal neighbourhood perimeter

This research proposes a new model for constructing decision trees using interval-valued fuzzy membership values. Most existing fuzzy decision trees do not consider the uncertainty associated with their membership values, however, precise values of fuzzy membership values are not always possible. In this paper, we represent fuzzy membership values as intervals to model uncertainty and employ the look-ahead based fuzzy decision tree induction method to construct decision trees. We also investigate the significance of different neighbourhood values and define a new parameter insensitive to specific data sets using fuzzy sets. Some examples are provided to demonstrate the effectiveness of the approach.     

A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach

Knowledge inference systems are built to identify hidden and logical patterns in huge data. Decision trees play a vital role in knowledge discovery but crisp decision tree algorithms have a problem with sharp decision boundaries which may not be implicated to all knowledge inference systems. A fuzzy decision tree algorithm overcomes this drawback. Fuzzy decision trees are implemented through fuzzification of the decision boundaries without disturbing the attribute values. Data reduction also plays a crucial role in many classification problems. In this research article, it presents an approach using principal component analysis and modified Gini index based fuzzy SLIQ decision tree algorithm. The PCA is used for dimensionality reduction, and modified Gini index fuzzy SLIQ decision tree algorithm to construct decision rules. Finally, through PID data set, the method is validated in the simulation experiment in MATLAB.     

Null values in fuzzy databases

Since in the real world, it often occurs that information is missing, database systems clearly need some facilities to deal with missing data. With respect to traditional database systems, the most commonly adopted approach to this problem is based on null values and three valued logic. This paper deals with the semantics and the use of null values in fuzzy databases. In dealing with missing information a distinction is made between incompleteness due to unavailability and incompleteness due to inapplicability. Both the database modelling and database querying aspects are described. With respect to attribute values, incompleteness due to unavailability is modelled by possibility distributions, which is a commonly used technique in the fuzzy databases. Domain specific null values, represented by a bottom symbol, are used to model incompleteness due to inapplicability. Extended possibilistic truth values are used to formalize the impact of data manipulation and (flexible) querying operations in the presence of these null values. The different cases of appearances of null values in the handling of selection conditions of flexible database queries are described in detail.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

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。     

区间值关系数据库上模糊关联规则的预测方法

讨论了区间值关系数据库上模糊关联规则的挖掘算法与预测方法。采用一种比RFCM算法省时的FCMdd算法将记录在属性的取值划分成若干个模糊集,并提出区间值关系数据库上模糊关联规则的挖掘算法。仿真实例说明挖掘算法能够通过挖掘有意义的模糊关联规则来发现区间值关系数据库中蕴涵的关联性。区间值关系数据库上模糊关联规则的预测方法改进了标准可加性模型,并通过遗传算法调整模糊关联规则中三角模糊数的参数来提高预测的精度。     

Extending the fuzzy database with fuzzy numbers

The fuzzy relational database model originated by the authors permits fuzzy domain values from a discrete, finite universe. The model is extended here by demonstrating that fuzzy numbers may be employed as domain values without loss of consistency with respect to representation or the relational algebra. Where equivalence is required in an ordinary relational database, similarity is employed in a fuzzy relational database. For discrete, finite universes, similarity between atomic elements is described via a fuzzy similarity relation with max-min transitivity. Two or more fuzzy numbers are defined to be α-similar if their union forms a continuous α-level set over the real line. This convention effects the partitioning of fuzzy number domains that is necessary to assure the well-definedness of the fuzzy relational algebra.