Online mining of fuzzy multidimensional weighted association rules

This paper addresses the integration of fuzziness with On-Line Analytical Processing (OLAP) based association rules mining. It contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube for knowledge discovery. A data cube is mainly constructed to provide users with the flexibility to view data from different perspectives as some dimensions of the cube contain multiple levels of abstraction. The first step of the process described in this paper involves introducing fuzzy data cube as a remedy to the problem of handling quantitative values of dimensional attributes in a cube. This facilitates the online mining of fuzzy association rules at different levels within the constructed fuzzy data cube. Then, we investigate combining the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules from the constructed fuzzy data cube. For this purpose, three different methods are introduced for single dimension, multidimensional and hybrid (integrates the other two methods) fuzzy weighted association rules mining. Each of the three methods utilizes a fuzzy data cube constructed to suite the particular method. To the best of our knowledge, this is the first effort in this direction. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results obtained for each of the three methods on a synthetic dataset and on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach. OLAP is one of the most popular tools for on-line, fast and effective multidimensional data analysis. In the OLAP framework, data is mainly stored in data hypercubes (simply called cubes).     

Modular fuzzy-reinforcement learning approach with internal model capabilities for multiagent systems.

To date, many researchers have proposed various methods to improve the learning ability in multiagent systems. However, most of these studies are not appropriate to more complex multiagent learning problems because the state space of each learning agent grows exponentially in terms of the number of partners present in the environment. Modeling other learning agents present in the domain as part of the state of the environment is not a realistic approach. In this paper, we combine advantages of the modular approach, fuzzy logic and the internal model in a single novel multiagent system architecture. The architecture is based on a fuzzy modular approach whose rule base is partitioned into several different modules. Each module deals with a particular agent in the environment and maps the input fuzzy sets to the action Q-values; these represent the state space of each learning module and the action space, respectively. Each module also uses an internal model table to estimate actions of the other agents. Finally, we investigate the integration of a parallel update method with the proposed architecture. Experimental results obtained on two different environments of a well-known pursuit domain show the effectiveness and robustness of the proposed multiagent architecture and learning approach.     

基于数据立方体的多维关联规则的挖掘方法

总结了现有基于数据立方体的多维关联规则挖掘算法，在此基础上将联机分析处理(OLAP)的钻取操作引入关联规则挖掘过程，提出Apriori_cubc算法的改进算法。通过动态调整立方体的维层次，来挖掘出用户感兴趣的关联规则。     

Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning

This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior.     

二维立方体中关联规则挖掘算法研究

针对二维数据立方体的结构特点 ,通过对传统的关联规则挖掘算法的改进 ,提出了一种二维立方体关联规则挖掘的新颖算法。该算法通过有效组织挖掘过程中的数据结构 ,降低对立方体的扫描次数 ,并充分利用联机分析处理技术 ,从而大大降低了执行时间 ,提高了执行效率     

联机分析关联规则挖掘的研究

在对关联挖掘、数据仓库、OLAP研究的基础之上，提出了联机分析关联规则挖掘的方法，并给出了针对该方法的特定算法。研究表明，同孤立的关联规则挖掘方法相比，该方法具有较大的灵活性和更高的效率。     

数据立方梯度挖掘的研究

1 前言随着人们生成、收集和存储数字化数据能力的极大提高,当今世界面临着各种原始数据的爆炸性增长。数据库技术的巨大进步创建了对大量数据的有效存储,成千上万的大型数据库被广泛地应用在商业、政府和科研等等部门。大量数据资源的积累为人们从历史数据中发现有用信息提供了基础,人们期望数据库能够提供智能化或者至少是半自动化的数据分析处理的能力。于是,数据仓库技术(Data Warehouse)、联机分析处理技术(On Line Analysis Processing)以及数据挖掘技术(Data Mining)应运而生。     

Mining fuzzy specific rare itemsets for education data

Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may have available relatively infrequent data, as well as frequent data. From infrequent data, we can find a set of rare itemsets that will be useful for teachers to find out which students need extra help in learning. While the previous association rules discovery techniques are able to discover some rules based on frequency, this is insufficient to determine the importance of a rule composed of frequency-based data items. To remedy this problem, we develop a new algorithm based on the Apriori approach to mine fuzzy specific rare itemsets from quantitative data. Finally, fuzzy association rules can be generated from these fuzzy specific rare itemsets. The patterns are useful to discover learning problems. Experimental results show that the proposed approach is able to discover interesting and valuable patterns from the survey data.     

教学评价与知识发现系统的设计

提出了一个基于联机分析技术（OLAP）的教学评价与知识发现,给出了由学生,知识点和类别等构成的六个维度的数据立方体以及利用OLAP技术和关联规则对该数据立方体进行数据挖掘的解决方案。利用上述方法对学生的考试系统进行挖掘,得出有用的结论,从而指导学校的教学工作。     

数据挖掘技术初探

数据挖掘技术已成为机器学习、数据库系统、人工智能等领域内热门的研究方向 .本文将讨论数据挖掘的基本概念 ,并在此基础上介绍、分析挖掘关联规则技术、决策树、聚类分析、数据管道等常用数据挖掘技术     

Coordinated learning by exploiting sparse interaction in multiagent systems

Multiagent learning provides a promising paradigm to study how autonomous agents learn to achieve coordinated behavior in multiagent systems. In multiagent learning, the concurrency of multiple distributed learning processes makes the environment nonstationary for each individual learner. Developing an efficient learning approach to coordinate agents’ behavior in this dynamic environment is a difficult problem especially when agents do not know the domain structure and at the same time have only local observability of the environment. In this paper, a coordinated learning approach is proposed to enable agents to learn where and how to coordinate their behavior in loosely coupled multiagent systems where the sparse interactions of agents constrain coordination to some specific parts of the environment. In the proposed approach, an agent first collects statistical information to detect those states where coordination is most necessary by considering not only the potential contributions from all the domain states but also the direct causes of the miscoordination in a conflicting state. The agent then learns to coordinate its behavior with others through its local observability of the environment according to different scenarios of state transitions. To handle the uncertainties caused by agents’ local observability, an optimistic estimation mechanism is introduced to guide the learning process of the agents. Empirical studies show that the proposed approach can achieve a better performance by improving the average agent reward compared with an uncoordinated learning approach and by reducing the computational complexity significantly compared with a centralized learning approach. Copyright © 2012 John Wiley & Sons, Ltd.     

基于特定角色上下文的多智能体Q学习

One of the main problems in cooperative multiagent learning is that the joint action space grows exponentially with the number of agents. In this paper, we investigate a sparse representation of the coordination dependencies between agents to employ roles and context-specific coordination graphs to reduce the joint action space. In our framework, the global joint Q-function is decomposed into a number of local Q-functions. Each local Q-function is shared among a small group of agents and is composed of a set of value rules. We propose a novel multiagent Q-learning algorithm which learns the weights in each value rule automatically. We give empirical evidence to show that our learning algorithm converges to the same optimal policy with a significantly faster speed than traditional multiagent learning techniques.     

OLAP关联规则挖掘

该文提出一种新的关联规则挖掘方法,OLAP关联规则挖掘。OLAP关联规则挖掘是OLAP技术和一些高效的关联规则挖掘算法的结合。OLAP关联规则挖掘方法是一种灵活的、多维的、多层次的高性能方法。该文首先介绍了O-LAP关联规则挖掘的结构,最后详述了OLAP关联规则挖掘的具体实现。     

A Complexity Guided Algorithm for Association Rule Extraction on Fuzzy DataCubes

The use of online analytical processing (OLAP) systems as data sources for data mining techniques has been widely studied and has resulted in what is known as online analytical mining (OLAM). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support imprecision. We, therefore, need OLAM methods which are able to deal with this imprecision. Association rules are one of the most used data mining techniques. There are several proposals that enable the extraction of association rules on DataCubes but few of these deal with imprecision in the process. The main problem observed in these proposals is the complexity of the rule set obtained. In this paper, we present a novel association rule extraction method that works over a fuzzy multidimensional model which is capable of representing and managing imprecise data. Our method deals with the problem of reducing the complexity of the result obtained by using fuzzy concepts and a hierarchical relation between them.     

OLAP中基于FP-增长的关联规则挖掘

关联规则挖掘是一种发现属性问关系的方法，主要用于在商务事务记录中挖掘事务问关系。本文将已经广泛使用的FP-增长(frequent-pattern growth，频繁模式增长)算法进行改进，实现了OLAP中的关联规则挖掘。改进算法分别针对单维、多维、混合维三种关联规则，将多维立方体转化成不同的关系表，通过关系表产生关联规则．并利用立方体中的事实值作为进一步约束，生成了更有价值的规则。     

基于数据立方体的关联规则挖掘及其实现

联机分析处理和数据挖掘是两种重要的数据分析方法。使用数据立方体作为数据存储结构,将两者集成起来,使得用户可以从不同角度、不同抽象层次分析数据。针对数据立方体的特点,本文提出了挖掘维间关联规则的算法,并编程实现了该算法,取得满意的结果。     

A multi-level ant-colony mining algorithm for membership functions

Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of binary bits to encode the membership functions. This paper elaborates on the original approach to increase the accuracy of results by adding multi-level processing. A multi-level ant colony framework is thus designed and an algorithm based on the structure is proposed to achieve the purpose. The proposed approach first transforms the fuzzy mining problem into a multi-stage graph, with each route representing a possible set of membership functions. The new approach then extends the previous one, using multi-level processing to solve the problem in which the maximum quantities of item values in the transactions may be large. The membership functions derived in a given level will be refined in the subsequent level. The final membership functions in the last level are then outputted to the rule-mining phase to find fuzzy association rules. Experiments are also performed to show the performance of the proposed approach. The experimental results show that the proposed multi-level ant colony systems mining approach can obtain improved results.     

图像数据高置信度关联规则的提取方法研究

在图像关联规则挖掘的某些领域,要求提取出具有较高置信度的关联规则,同时对支持度的要求相对较低。提出了一种在兼顾支持度的情况下挖掘出高置信度的图像关联规则的方法。为了便于有效地提取图像关联规则,使用了名为bSQ（bit Sequential）的一种栅格数据格式。而后采取“逐层搜索”的方法,建立规则树,避免了传统方法在处理低支持度时产生的大量频繁项集。最后通过多图像关联规则提取优先级和图像数据立方体等技术在多幅图像中提取基于象素级的关联规则。通过实验证明,该方法能有效地提取图像数据高置信度关联规则,方法具有可行性。     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.     

Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier. Our approach is mainly composed of two phases. First, a large number of candidate rules are generated for each class using fuzzy association rules mining, and they are pre-screened using two rule evaluation criteria in order to reduce the fuzzy rule search space. Candidate rules obtained after pre-screening are used in genetic fuzzy classifier to generate rules for the classes specified in IIDS: namely Normal, PRB-probe, DOS-denial of service, U2R-user to root and R2L-remote to local. During the next stage, boosting genetic algorithm is employed for each class to find its fuzzy rules required to classify data each time a fuzzy rule is extracted and included in the system. Boosting mechanism evaluates the weight of each data item to help the rule extraction mechanism focus more on data having relatively more weight, i.e., uncovered less by the rules extracted until the current iteration. Each extracted fuzzy rule is assigned a weight. Weighted fuzzy rules in each class are aggregated to find the vote of each class label for each data item.