基于模糊概念图的文档聚类及其在Web中的应用

随着World Wide Web上数据量的日益庞大,现有的搜索引擎已经不能满足用户日益增长的需求.利用数据挖掘技术,提高搜索效率,实现了查询的用户化.首先提出了模糊概念图的模型来描述词语间的关系,然后在聚类过程中引入概念知识,提出了基于模糊概念图的文档聚类算法,通过分析用户的浏览行为发现兴趣模式.在上述技术的基础上,给出了一种用户化的智能搜索系统的实现策略,通过分析概念间的关系和用户的兴趣模式,评价超链/文档和查询的相关程度,从而帮助用户得到更准确的信息.     

Novel fuzzy test patterns and their application in the measurement of geometric characteristics of displays

One of the final steps in a display production line is the image alignment that includes the visual adjustment of the geometric parameters and the color of the image. Measurement of geometric characteristics using machine vision is a necessary function in the automatic alignment of displays’ image in the factory. A critical part in the measurement of the geometric attributes is to precisely locate a test pattern position on the display screen. In this paper we introduce novel patterns as fuzzy test patterns and present a novel algorithm to precisely locate the fuzzy test pattern in captured images of the display screen. We experimentally show that the application of the proposed fuzzy test pattern and its associated locating algorithm increases the precision and robustness of the geometric measurements of a display like a TV display. The use of this new measurement method in an auto-alignment system increases the adjustment accuracy, improves the reliability of the alignment system, and improves the quality of images on the display of the adjusted display sets.     

Defending against XML-related attacks in e-commerce applications with predictive fuzzy associative rules

Security administrators need to prioritise which feature to focus on amidst the various possibilities and avenues of attack, especially via Web Service in e-commerce applications. This study addresses the feature selection problem by proposing a predictive fuzzy associative rule model (FARM). FARM validates inputs by segregating the anomalies based fuzzy associative patterns discovered from five attributes in the intrusion datasets. These associative patterns leads to the discovery of a set of 18 interesting rules at 99% confidence and subsequently, categorisation into not only certainly allow/deny but also probably deny access decision class. FARM's classification provides 99% classification accuracy and less than 1% false alarm rate. Our findings indicate two benefits to using fuzzy datasets. First, fuzzy enables the discovery of fuzzy association patterns, fuzzy association rules and more sensitive classification. In addition, the root mean squared error (RMSE) and classification accuracy for fuzzy and crisp datasets do not differ much when using the Random Forest classifier. However, when other classifiers are used with increasing number of instances on the fuzzy and crisp datasets, the fuzzy datasets perform much better. Future research will involve experimentation on bigger data sets on different data types.     

A robust technique based on invariant moments – ANFIS for recognition of human parasite eggs in microscopic images

In this study, we propose a robust technique based on invariant moments – adaptive network based fuzzy inference system (IM-ANFIS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of IM-ANFIS approach used in this study. Recently, the pattern recognition principles have come into prominence. The pattern recognition includes operation and design of systems that recognize patterns in data sets. Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection. The technique presented in this study enables to classify 16 different parasite eggs from their microscopic images. This proposed recognition method includes three stages. In first stage, a preprocessing subsystem is realized for obtaining unique features from the same group of patterns. In second stage, a feature extraction mechanism which is based on the invariant moments is used. In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process. We conduct computer simulations on MATLAB environment. The overall success rate is almost 95%.     

Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools

We consider linguistic data(base) summaries in the sense of Yager [Information Sciences 28 (1982) 69-86], exemplified by “most employees are young and well paid” (with some degree of truth added), for a personnel database, as an intuitive, human consistent and natural language based knowledge discovery tool. We present first an extension of the classic Yager’s approach to involve more sophisticated criteria of goodness, search methods, etc. We advocate the use of the concept of a protoform (prototypical form), that is recently vividly advocated by Zadeh [A prototype-centered approach to adding deduction capabilities to search engines—the concept of a protoform. BISC Seminar, University of California, Berkeley, 2002], as a general form of a linguistic data summary. We present an extension of our interactive approach, based on fuzzy logic and fuzzy database queries, which makes it possible to implement such linguistic data summaries. We show how fuzzy queries are related to linguistic summaries, and show that one can introduce a hierarchy of protoforms, or abstract summaries in the sense of latest Zadeh’s [A prototype-centered approach to adding deduction capabilities to search engines—the concept of a protoform. BISC Seminar, University of California, Berkeley, 2002] ideas meant mainly for increasing deduction capabilities of search engines. For illustration we show an implementation for a sales database in a computer retailer, employing some type of a protoform of a linguistic summary.     

Pattern discovery of fuzzy time series for financial prediction

A fuzzy time series data representation method based on the Japanese candlestick theory is proposed and used in assisting financial prediction. The Japanese candlestick theory is an empirical model of investment decision. The theory assumes that the candlestick patterns reflect the psychology of the market, and the investors can make their investment decision based on the identified candlestick patterns. We model the imprecise and vague candlestick patterns with fuzzy linguistic variables and transfer the financial time series data to fuzzy candlestick patterns for pattern recognition. A fuzzy candlestick pattern can bridge the gap between the investors and the system designer because it is visual, computable, and modifiable. The investors are not only able to understand the prediction process, but also to improve the efficiency of prediction results. The proposed approach is applied to financial time series forecasting problem for demonstration. By the prototype system which has been established, the investment expertise can be stored in the knowledge base, and the fuzzy candlestick pattern can also be identified automatically from a large amount of the financial trading data.     

Simulation of overall and local buckling behavior of cylindrical tubular members using fuzzy inference system

A fuzzy system has been developed to estimate the overall and local buckling behavior of cylindrical tubular members under monotonic axial compression. To train and test the fuzzy system, numerical data obtained from the finite element analyses is utilized. For this aim, a degenerate-continuum shell element which accounts for material and geometric nonlinearity is employed. Also, a least squares algorithm has been applied to determine the parameters of the fuzzy system such that the resulting fuzzy system accomplishes the desired performance. The proposed fuzzy system is capable of tracing the complete load-shortening relation and provides a tool for faster analysis.     

基于犹豫模糊几何算法的网络系统决策模型

针对决策信息为犹豫模糊信息且准则之间存在相互关系的多准则群决策问题,提出了一种基于阿基米德T-范数和S-范数的广义的犹豫模糊信息几何B-平均(GHFGBM)集成算法,构建一种新的计算机网络系统选择决策模型,提出的GHFGBM不仅在信息集结的过程中能够考虑到输入变量之间的相互联系,还能够使得该模型方法应用于其他领域。在此过程中,研究了GHFGBM算法的几种基本特性,比如置换不变性、单调性、有界性和幂等性等等。紧接着,分别对参数和加性算子赋予不同的数值和函数,详细分析了GHFGBM算法的一系列常用算子表示。最后结合对计算机网络系统更新方案的选择实验,验证了提出的选择决策模型是合理的且是有效的。     

A geometric approach to cluster validity for normal mixtures

 We study indices for choosing the correct number of components in a mixture of normal distributions. Previous studies have been confined to indices based wholly on probabilistic models. Viewing mixture decomposition as probabilistic clustering (where the emphasis is on partitioning for geometric substructure) as opposed to parametric estimation enables us to introduce both fuzzy and crisp measures of cluster validity for this problem. We presume the underlying samples to be unlabeled, and use the expectation-maximization (EM) algorithm to find clusters in the data. We test 16 probabilistic, 3 fuzzy and 4 crisp indices on 12 data sets that are samples from bivariate normal mixtures having either 3 or 6 components. Over three run averages based on different initializations of EM, 10 of the 23 indices tested for choosing the right number of mixture components were correct in at least 9 of the 12 trials. Among these were the fuzzy index of Xie-Beni, the crisp Davies-Bouldin index, and two crisp indices that are recent generalizations of Dunn’s index. Received: 29 July 1997/Accepted: 1 September 1997     

A computational intelligence approach for a better diagnosis of diabetic patients

Knowledge discovery refers to identifying hidden and valid patterns in data and it can be used to build knowledge inference systems. Decision tree is one such successful technique for supervised learning and extracting knowledge or rules. This paper aims at developing a decision tree model to predict the occurrence of diabetes disease. Traditional decision tree algorithms have a problem with crisp boundaries. Much better decision rules can be identified from these clinical data sets with the use of the fuzzy decision boundaries. The key step in the construction of a decision tree is the identification of split points and in this work best split points are identified using the Gini index. Authors propose a method to minimize the calculation of Gini indices by identifying false split points and used the Gaussian fuzzy function because the clinical data sets are not crisp. As the efficiency of the decision tree depends on many factors such as number of nodes and the length of the tree, pruning of decision tree plays a key role. The modified Gini index-Gaussian fuzzy decision tree algorithm is proposed and is tested with Pima Indian Diabetes (PID) clinical data set for accuracy. This algorithm outperforms other decision tree algorithms.     

基于熵聚类模糊神经网络味觉信号识别系统的研究

提出了一种基于熵聚类的模糊神经网络味觉信号识别系统模型,该模型利用聚类方法实现模糊输入空间划分和模糊IF-THEN规则提取,并使用梯度下降法对系统参数进行精炼,系统兼具有良好的可解释性和学习能力,对11种矿泉水味觉信号的识别实验结果表明了该系统的可行性和有效性。     

Geometric Bonferroni Mean Operators

The Bonferroni mean has been extensively applied in multicriteria decision‐making and support system and developed intuitionistic fuzzy set theory. Based on the second interpretation of the Bonferroni mean, in this paper, we introduce the geometric Bonferroni mean, which is a generalization of the Bonferroni mean and geometric mean and generalized geometric Bonferroni mean, and investigate their properties. To describe the uncertainty and fuzziness more objectively, we further develop the intuitionistic fuzzy geometric Bonferroni mean and the generalized intuitionistic fuzzy geometric Bonferroni mean, which describe the relationship between arguments, and the weighted intuitionistic fuzzy geometric Bonferroni mean and the generalized weighted intuitionistic fuzzy geometric Bonferroni mean, which consider the importance of each argument. Finally, we investigate their properties in detail.     

Fuzzy pricing of geometric Asian options and its algorithm

Owing to the fluctuations of the financial market, input data in the options pricing formula cannot be expected to be precise. This paper discusses the problem of pricing geometric Asian options under the fuzzy environment. We present the fuzzy price of the geometric Asian option under the assumption that the underlying stock price, the risk-free interest rate and the volatility are all fuzzy numbers. This assumption makes the financial investors to pick any geometric Asian option price with an acceptable belief degree. In order to obtain the belief degree, the interpolation search algorithm has been proposed. Some numerical examples are presented to illustrate the rationality and practicability of the model and the algorithm. Finally, an empirical study is performed based on the real data. The empirical study results indicate that the proposed fuzzy pricing model of geometric Asian option is a useful tool for modeling the imprecise problem in the real world.     

A validity measure for fuzzy clustering

The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data. This function depends on the data set, geometric distance measure, distance between cluster centroids and more importantly on the fuzzy partition generated by any fuzzy algorithm used. The function is mathematically justified via its relationship to a well-defined hard clustering validity function, the separation index for which the condition of uniqueness has already been established. The performance of this validity function compares favorably to that of several others. The application of this validity function to color image segmentation in a computer color vision system for recognition of IC wafer defects which are otherwise impossible to detect using gray-scale image processing is discussed     

Reducing the number of parameters of a fuzzy system using scaling functions

Learning techniques are tailored for fuzzy systems in order to tune them or even for deriving fuzzy rules from data. However, a compromise between accuracy and interpretability has to be found. Flexible fuzzy systems with a large number of parameters and high degrees of freedom tend to function as black boxes. In this paper, we introduce an interpretation of fuzzy systems that enables us to work with a small number of parameters without loosing flexibility or interpretability. In this way, we can provide a learning algorithm that is efficient and yields accuracy as well as interpretability. Our fuzzy system is based on extremely simple fuzzy sets and transformations using interpretable scaling functions of the input variables.     

A fuzzy temporal notation and its application to specify fault patterns for diagnosis

Many systems collect vast amounts of data over time, which is used to perform critical tasks like diagnosis, surveillance, resource management, planning and forecasting. To effectively use the historical data for these purposes, it is important to analyze the data and to gain insight into its significant aspects, by identifying the presence and characteristics of specific patterns. We describe a fuzzy logical notation, enhanced with facilities for expressing approximate temporal patterns, to build compositional and abstract models of syntactic structure of patterns. We present an algorithm, which detects where and how strongly the given pattern (i.e., a formula) is present. The approach is illustrated by specifying and detecting fault patterns for trace-based diagnosis of dynamic systems.     

Using a fuzzy approach to support financial analysis in the corporate acquisition process

In the global market place, many companies have had to adapt their strategies to meet significant challenges. A strategy adopted by some companies has been international expansion via acquisitions. The need for expert knowledge to determine an appropriate company to acquire has been complicated by the sheer size of the global market place. The costs associated with this in relation to time and personnel have created the need for a computerised expert system to be developed. This paper endeavours to show how a proposed fuzzy based system can assist in the identification of a company for acquisition. The authors discuss the manipulation of the magnitude of fuzzy membership functions to communicate priorities within the system. The fuzzy system is designed to assist financial experts in identifying a suitable company for acquisition in the corporate acquisition process. This includes the deliberate weighting of certain inputs and results above others in the decision-making process. The system attempts to learn and simulate the human precedence given to particular financial statistics in company analysis. The system uses the magnitude of the fuzzy membership functions to reflect the human precedence given to each financial ratio. This enables a particular company's strengths and weakness to be considered while concurrently considering their significance and relevance to the acquiring organisation. The system will enable a larger number of companies to be analysed in a more time and cost-effective manner. The development of this system is intended to illustrate that a fuzzy system can aid the financial experts of an acquiring organisation in the global acquisition process.     

A Fast Geometric Method for Defuzzification of Type-2 Fuzzy Sets

Generalized type-2 fuzzy logic systems cannot currently be used for practical problems because the amount of computation required to defuzzify a generalized type-2 fuzzy set is too large. This paper presents a new method for defuzzifing a type-2 fuzzy set. The new much faster technique is based on geometric representations and operations. The results of a real world example contained in this paper show this new approach to be over 200 000 times faster than type-reduction. We present a new method for assessing the accuracy of the membership function of a type-2 fuzzy set. This method is used to show that the new representation used by the defuzzifier is not detrimental to the accuracy of the set. We also discuss the differences between the new approach and type-reduction, identifying the origin of this massive improvement in execution speed.      

基于模糊数据挖掘和遗传算法的网络入侵检测技术

文章通过开发一套新的网络入侵检测系统来证实应用模糊逻辑和遗传算法的数据挖掘技术的有效性;这个系统联合了基于模糊数据挖掘技术的异常检测和基于专家系统的滥用检测,在开发异常检测的部分时,利用模糊数据挖掘技术来从正常的行为存储模式中寻找差异,遗传算法用来调整模糊隶属函数和选择一个合适的特征集合,滥用检测部分用于寻找先前行为描述模式,这种模式很可能预示着入侵,网络的通信量和系统的审计数据被用做两个元件的输入;此系统的系统结构既支持异常检测又支持滥用检测、既适用于个人工作站又可以适用于复杂网络。