Generalized weighted conditional fuzzy clustering

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. Among many existing modifications of this method, conditional or context-dependent c-means is the most interesting one. In this method, data vectors are clustered under conditions based on linguistic terms represented by fuzzy sets. This paper introduces a family of generalized weighted conditional fuzzy c-means clustering algorithms. This family include both the well-known fuzzy c-means method and the conditional fuzzy c-means method. Performance of the new clustering algorithm is experimentally compared with fuzzy c-means using synthetic data with outliers and the Box-Jenkins database.     

The concept of conditional fuzzy measure

In this article a concept of conditional fuzzy measure is presented, which is a generalization of conditional probability measure. Its properties are studied in the general case and in some particular types of fuzzy measures as representable measures, capacities of order two, and belief-plausibility measures. In the case of capacities of order two it coincides with the concept given by Dempster for representable measures. However, it differs from the Dempster's rule for conditioning belief-plausibility measures. As it is shown, Dempster's rule of conditioning is based on the idea of combining information and our definition is based on a restriction in the set of possible worlds.     

一种扩展的条件模糊C-均值聚类算法

在综合分析标准的模糊C-均值聚类算法和条件模糊C-均值聚类算法基础上,对模糊划分空间进行修改,进一步弱化模糊划分矩阵的约束,给出一种扩展的条件模糊C-均值聚类算法。算法的划分矩阵和原型不依赖于背景约束及模糊划分矩阵的隶属度总和。实验结果表明：该算法可以得到不同的聚类原型,并具有很好的聚类效果。     

Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP

The lack of consistency in decision making can lead to inconsistent conclusions. In fuzzy analytic hierarchy process (fuzzy AHP) method, it is difficult to ensure a consistent pairwise comparison. Furthermore, establishing a pairwise comparison matrix requires judgments for a level with n criteria (alternatives). The number of comparisons increases as the number of criteria increases. Therefore, the decision makers judgments will most likely be inconsistent. To alleviate inconsistencies, this study applies fuzzy linguistic preference relations (Fuzzy LinPreRa) to construct a pairwise comparison matrix with additive reciprocal property and consistency. In this study, the fuzzy AHP method is reviewed, and then the Fuzzy LinPreRa method is proposed. Finally, the presented method is applied to the example addressed by Kahraman et al. [C. Kahraman, D. Ruan, I. Do?an, Fuzzy group decision making for facility location selection, Information Sciences 157 (2003) 135-153]. This study reveals that the proposed method yields consistent decision rankings from only n − 1 pairwise comparisons, which is the same result as in Kahraman et al. research. The presented fuzzy linguistic preference relations method is an easy and practical way to provide a mechanism for improving consistency in fuzzy AHP method.     

模糊时序关联规则挖掘

借助模糊概念和模糊运算，对时间区间的描述很容易实现。对于指定的日历模式，不同的时间区间可根据它们的隶属度具有不同的权重。在模糊日历代数基础上，结合增量挖掘和累进计数的思想，提出了一种基于模糊日历的模糊时序关联规则挖掘方法。理论分析和实验结果均表明，该算法是高效可行的。     

Maintaining data temporal consistency in distributed real-time systems

Previous works on maintaining temporal consistency of real-time data objects mainly focuses on real-time database systems in which the transmission delays (jitters) of update jobs are simply ignored. However, this assumption does not hold in distributed real-time systems where the jitters of the update jobs can be large and change unpredictably with time. In this paper, we examine the design problems when the More-Less (ML) approach (Xiong and Ramamritham in Proc. of the IEEE real-time systems symposium 1999; IEEE Trans Comput 53:567?C583, 2004), known to be an efficient scheme for maintaining temporal consistency of real-time data objects, is applied in a distributed real-time system environment. We propose two new extensions based on ML, called Jitter-based More-Less (JB-ML) and Statistical Jitter-based More-Less (SJB-ML) to address the jitter problems. JB-ML assumes that in the system the jitter is a constant for each update task, and it provides a deterministic guarantee in temporal consistency of the real-time data objects. SJB-ML further relaxes this restriction and provides a statistical guarantee based on the given QoS requirements of the real-time data objects. We demonstrate through extensive simulation experiments that both JB-ML and SJB-ML are effective approaches and they significantly outperform ML in terms of improving schedulability.     

基于群体一致性强度的模糊评估方法

针对模糊信息的定量评估难题, 在研究多属性决策问题的基础上提出基于群体一致性强度的模糊评估方法. 利用证据理论合成评估值后, 引入语义折扣因子的概念, 将区间评估信息转化为确定的评估值; 设计群体一致性强度 指标, 最优化语义折扣因子的取值, 使得评估方法能够体现评估中的主流意见; 通过软件可信评估实例验证了所提出方法可有效解决模糊评估信息的分析和合成问题.     

Discovery of fuzzy temporal association rules

We propose a data mining system for discovering interesting temporal patterns from large databases. The mined patterns are expressed in fuzzy temporal association rules which satisfy the temporal requirements specified by the user. Temporal requirements specified by human beings tend to be ill-defined or uncertain. To deal with this kind of uncertainty, a fuzzy calendar algebra is developed to allow users to describe desired temporal requirements in fuzzy calendars easily and naturally. Fuzzy operations are provided and users can define complicated fuzzy calendars to discover the knowledge in the time intervals that are of interest to them. A border-based mining algorithm is proposed to find association rules incrementally. By keeping useful information of the database in a border, candidate itemsets can be computed in an efficient way. Updating of the discovered knowledge due to addition and deletion of transactions can also be done efficiently. The kept information can be used to help save the work of counting and unnecessary scans over the updated database can be avoided. Simulation results show the effectiveness of the proposed system. A performance comparison with other systems is also given.     

Multiplicative consistency analysis for q-rung orthopair fuzzy preference relation

The q-rung orthopair fuzzy set is characterized by membership and nonmembership functions, and the sum of the qth power of them is less than or equal to one. Since it releases the constraints existed in both intuitionistic fuzzy set and Pythagorean fuzzy set, it has wide applications in real cases. However, so far, there is little research on the multiplicative consistency of q-rung orthopair fuzzy preference relation (q-ROFPR). To fill this vacancy, this paper provides a detailed analysis on the multiplicative consistency of q-ROFPR. First, we investigate the concept of multiplicative consistent q-ROFPR and its properties. Subsequently, two goal programming models are proposed to derive the priorities from individual and group q-ROFPRs, respectively. After that, a novel consistency-improving algorithm for q-ROFPR and a weight-generating method for decision-makers are discussed in detail, based on which, a novel group decision-making method is proposed. Finally, a case study concerning the evaluation of rehabilitation program selection is given to illustrate the applicability of the proposed method. The effectiveness and superiority of the proposed method are verified by comparing it with some existing methods.     

On earliest deadline first scheduling for temporal consistency maintenance

A real-time object is one whose state may become invalid with the passage of time. A temporal validity interval is associated with the object state, and the real-time object is temporally consistent if its temporal validity interval has not expired. Clearly, the problem of maintaining temporal consistency of data is motivated by the need for a real-time system to track its environment correctly. Hence, sensor transactions must be able to execute periodically and also each instance of a transaction should perform the relevant data update before its deadline. Unfortunately, the period and deadline assignment problem for periodic sensor transactions has not received the attention that it deserves. An exception is the More-Less scheme, which uses the Deadline Monotonic (DM) algorithm for scheduling periodic sensor transactions. However, there is no work addressing this problem from the perspective of dynamic priority scheduling. In this paper, we examine the problem of temporal consistency maintenance using the Earliest Deadline First (EDF) algorithm in three steps: First, the problem is transformed to another problem with a sufficient (but not necessary) condition for feasibly assigning periods and deadlines. An optimal solution for the problem can be found in linear time, and the resulting processor utilization is characterized and compared to a traditional approach. Second, an algorithm to search for the optimal periods and deadlines is proposed. The problem can be solved for sensor transactions that require any arbitrary deadlines. However, the optimal algorithm does not scale well when the problem size increases. Hence, thirdly, we propose a heuristic search-based algorithm that is more efficient than the optimal algorithm and is capable of finding a solution if one exists.

Quality of service guarantee for temporal consistency of real-time transactions

The more-less (ML) scheme has been shown to be an efficient approach for maintaining temporal consistency of real-time data objects. Although ML provides a deterministic guarantee in temporal consistency, the number of update transactions that can be supported in a system is limited. This is due to its use of the worst-case computation time in deriving deadlines and periods of update transactions. This paper studies the problem of temporal consistency maintenance where a certain degree of temporal inconsistency is tolerable. A suite of statistical more-less (SML) approaches are proposed to explore the trade-off between quality of service (QoS) of temporal consistency and the number of supported transactions. It begins with a baseline algorithm, SML-BA, which provides the requested QoS of temporal consistency. Then, SML with optimization (SML-OPT) is proposed to further improve the QoS by better utilizing the excess processor capacity. Finally, SML-OPT is enhanced with a slack reclaiming scheme (SML-SR). The reclaimed slacks are used to process jobs whose required computation time is larger than the guaranteed computation time. Simulation experiments are conducted to compare the performance of these schemes (SML-BA, SML-OPT, and SML-SR) together with the deterministic more-less and half-half schemes. The results show that the SML schemes are effective in trading the schedulability of transactions for the QoS guaranteed. Moreover, SML-SR performs best and offers a significant QoS improvement over SML-BA and SML-OPT.     

Maintaining temporal consistency: pessimistic vs. optimisticconcurrency control

We study the performance of concurrency control algorithms in maintaining temporal consistency of shared data in hard real time systems. In our model, a hard real time system consists of periodic tasks which are either write only, read only or update transactions. Transactions may share data. Data objects are temporally inconsistent when their ages and dispersions are greater than the absolute and relative thresholds allowed by the application. Real time transactions must read temporally consistent data in order to deliver correct results. Based on this model, we have evaluated the performance of two well known classes of concurrency control algorithms that handle multiversion data: the two phase locking and the optimistic algorithms, as well as the rate monotonic and earliest deadline first scheduling algorithms. The effects of using the priority inheritance and stack based protocols with lock based concurrency control are also studied     

Incremental fuzzy temporal association rule mining using fuzzy grid table

Traditional temporal association rules mining algorithms cannot dynamically update the temporal association rules within the valid time interval with increasing data. In this paper, a new algorithm called incremental fuzzy temporal association rule mining using fuzzy grid table (IFTARMFGT) is proposed by combining the advantages of boolean matrix with incremental mining. First, multivariate time series data are transformed into discrete fuzzy values that contain the time intervals and fuzzy membership. Second, in order to improve the mining efficiency, the concept of boolean matrices was introduced into the fuzzy membership to generate a fuzzy grid table to mine the frequent itemsets. Finally, in view of the Fast UPdate (FUP) algorithm, fuzzy temporal association rules are incrementally mined and updated without repeatedly scanning the original database by considering the lifespan of each item and inheriting the information from previous mining results. The experiments show that our algorithm provides better efficiency and interpretability in mining temporal association rules than other algorithms.

     

Dynamic temporal interpretation contexts for temporal abstraction

Temporal abstraction is the task of abstracting higher‐level concepts from time‐stamped data in a context‐sensitive manner. We have developed and implemented a formal knowledge‐based framework for decomposing and solving that task that supports acquisition, maintenance, reuse, and sharing of temporal‐abstraction knowledge. We present the logical model underlying the representation and runtime formation of interpretation contexts. Interpretation contexts are relevant for abstraction of time‐oriented data and are induced by input data, concluded abstractions, external events, goals of the temporal‐abstraction process, and certain combinations of interpretation contexts. Knowledge about interpretation contexts is represented as a context ontology and as a dynamic induction relation over interpretation contexts and other proposition types. Induced interpretation contexts are either basic, composite, generalized, or nonconvex. We provide two examples of applying our model using an implemented system; one in the domain of clinical medicine (monitoring of diabetes patients) and one in the domain of traffic engineering (evaluation of traffic‐control actions). We discuss several distinct advantages to the explicit separation of interpretation‐context propositions from the propositions inducing them and from the abstractions created within them. This revised version was published online in June 2006 with corrections to the Cover Date.     

Models of representation of fuzzy temporal knowledge in databases of temporal series

the problem of representation of fuzzy knowledge in databases of temporal series is considered. The existing approaches to formalization and search of temporal knowledge in such databases are described. A new class of models for representation of fuzzy temporal knowledge in databases of time series is proposed in order to support the processes of knowledge revealing. The methodology of estimating the efficiency of representations based on the use of general-system principles of human thinking and speaking communication is presented. Based on the selected system of criteria, the advantages of the proposed class of models are substantiated.     

Methods for fuzzy complementary preference relations based on multiplicative consistency

To improve the consistency of a preference relation is a hot topic in decision making. Wang and Chen (2008) gave a simple method to construct the complete fuzzy complementary preference relation from only n − 1 pairwise comparisons. However, some values may not be in the defined scope and need to be transformed, and thus some original information may be lost in the transformation process. In this paper, we propose a new method to avoid this issue based on the multiplicative consistency of the fuzzy complementary preference relation and apply it to fuzzy Analytic Hierarchy Process (AHP). An example is further given to illustrate our method.     

模糊互补判断矩阵一致性检验和改进方法研究

针对模糊互补判断矩阵的一致性修正问题,指出了模糊互补判断矩阵一致性修正方法的不足,从模糊一致矩阵传统定义出发,讨论了检验模糊判断矩阵是否满足完全一致性的方法,推导出模糊一致性指标,并给出了模糊判断矩阵一致性改进的方法,从理论上分析了该算法的可行性,这种算法不仅简便实用,而且为专家对原始判断信息进行针对性修正提供了参考依据.     

Group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency

In this paper, we present a new method for group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency. We estimate unknown preference values based on the additive consistency and then construct the consistency matrix which satisfies the additive consistency and the order consistency simultaneously for aggregation. The existing group decision making methods may not satisfy the order consistency for aggregation in some situations. The proposed method can overcome the drawback of the existing methods. It provides us with a useful way for group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency.     

命题模糊逻辑系统中有限理论的弱相容度

推广了命题模糊逻辑系统中有限理论相容性的概念。以理论Г是否同时推出命题A和命题┐A为基准点引进了有限理论弱相容度的新概念,讨论了其性质,并给出了判定它大小的一系列准则。     

People detection through quantified fuzzy temporal rules

The knowledge about the position and movement of people is of great importance in mobile robotics for implementing tasks such as navigation, mapping, localization, or human-robot interaction. This knowledge enhances the robustness, reliability and performance of the robot control architecture. In this paper, a pattern classifier system for the detection of people using laser range finders data is presented. The approach is based on the quantified fuzzy temporal rules (QFTRs) knowledge representation and reasoning paradigm, that is able to analyze the spatio-temporal patterns that are associated to people. The pattern classifier system is a knowledge base made up of QFTRs that were learned with an evolutionary algorithm based on the cooperative-competitive approach together with token competition. A deep experimental study with a Pioneer II robot involving a five-fold cross-validation and several runs of the genetic algorithm has been done, showing a classification rate over 80%. Moreover, the characteristics of the tests represent complex and realistic conditions (people moving in groups, the robot moving in part of the experiments, and the existence of static and moving people).