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。     

Mining fuzzy temporal association rules by item lifespans

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In real-world applications, transactions may contain quantitative values and each item may have a lifespan from a temporal database. In this paper, we thus propose a data mining algorithm for deriving fuzzy temporal association rules. It first transforms each quantitative value into a fuzzy set using the given membership functions. Meanwhile, item lifespans are collected and recorded in a temporal information table through a transformation process. The algorithm then calculates the scalar cardinality of each linguistic term of each item. A mining process based on fuzzy counts and item lifespans is then performed to find fuzzy temporal association rules. Experiments are finally performed on two simulation datasets and the foodmart dataset to show the effectiveness and the efficiency of the proposed approach.     

Visualizing and fuzzy filtering for discovering temporal trajectories of association rules

We propose a user-centric rule filtering method that allows to identify association rules that exhibit a certain user-specified temporal behavior with respect to rule evaluation measures. The method can considerably reduce the number of association rules that have to be assessed manually after a rule induction. This is especially necessary if the rule set contains many rules as it is the case for the task of finding rare patterns inside the data. For the proposed method, we will reuse former work on the visualization of association rules [M. Steinbrecher, R. Kruse, Visualization of possibilistic potentials, in: Foundations of Fuzzy Logic and Soft Computing, in: Lecture Notes in Comput. Sci., vol. 4529, Springer-Verlag, Berlin/Heidelberg, 2007, pp. 295–303] and use an extension of it to motivate and assess the presented filtering technique. We put the focus on rules that are induced from a data set that contains a temporal variable and build our approach on the requirement that temporally ordered sets of association rules are available, i.e., one set for every time frame. To illustrate this, we propose an ad-hoc learning method along the way. The actual rule filtering is accomplished by means of fuzzy concepts. These concepts use linguistic variables to partition rule-related domains of interest, such as the confidence change rate. The original rule sets are then matched against these user concepts and result in only those rules that match the respective concepts to a predefined extent. We provide empirical evidence by applying the proposed methods to hand-crafted as well as real-world data sets and critically discuss the current state and further prospects.     

Mining fuzzy periodic association rules

We develop techniques for discovering patterns with periodicity in this work. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e., the periodicity. Periodicities in the database are usually not very precise and have disturbances, and might occur at time intervals in multiple time granularities. To overcome these difficulties and to be able to discover the patterns with fuzzy periodicity, we propose the fuzzy periodic calendar which defines fuzzy periodicities. Furthermore, we develop algorithms for mining fuzzy periodicities and the fuzzy periodic association rules within them. Experimental results have shown that our method is effective in discovering fuzzy periodic association rules.     

Mining fuzzy quantitative association rules

Abstract: The concept of fuzzy sets is one of the most fundamental and influential tools in the development of computational intelligence. In this paper the fuzzy pincer search algorithm is proposed. It generates fuzzy association rules by adopting combined top-down and bottom-up approaches. A fuzzy grid representation is used to reduce the number of scans of the database and our algorithm trims down the number of candidate fuzzy grids at each level. It has been observed that fuzzy association rules provide more realistic visualization of the knowledge extracted from databases.     

加权模糊关联挖掘算法

针对数量型关联规则挖掘中划分边界过硬问题,以及加权关联规则中为确保向下封闭性成立而引起的规则丢失问题,提出一种新的加权模糊关联挖掘模型及其挖掘算法 NFWARM.为了避免区间划分引起的边界过硬问题,该模型引入模糊集软化属性的划分边界;同时,使用属性权重刻画元素对规则的贡献,在保证频繁项集向下封闭性的情况下,不会引起规则丢失.实验结果表明,该算法适用于包含布尔型和数值型数据的大型数据库的规则挖掘,并且得到的频繁项目集数目和规则数目有显著增加.     

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).     

一种加权时态关联规则挖掘算法

许多现实数据库都存在时态语义问题,因此在挖掘关联规则时附加上时态约束会使规则更具有实际意义。但目前提出的大多数时态关联规则挖掘算法,一般都认为每个数据项的重要性相同,而从决策者角度出发,往往会优先考虑利润较高的项目。提出了一种加权时态关联规则挖掘算法,以项目的生命周期作为时间特征,允许用户设定不同的项目权重。实验结果证明,该算法不仅能有效地发现加权时态关联规则,而且挖掘出的规则更有价值。     

Mining fuzzy association rules from questionnaire data

Association rule mining is one of most popular data analysis methods that can discover associations within data. Association rule mining algorithms have been applied to various datasets, due to their practical usefulness. Little attention has been paid, however, on how to apply the association mining techniques to analyze questionnaire data. Therefore, this paper first identifies the various data types that may appear in a questionnaire. Then, we introduce the questionnaire data mining problem and define the rule patterns that can be mined from questionnaire data. A unified approach is developed based on fuzzy techniques so that all different data types can be handled in a uniform manner. After that, an algorithm is developed to discover fuzzy association rules from the questionnaire dataset. Finally, we evaluate the performance of the proposed algorithm, and the results indicate that our method is capable of finding interesting association rules that would have never been found by previous mining algorithms.     

Mining fuzzy association rules from uncertain data

Association rule mining is an important data analysis method that can discover associations within data. There are numerous previous studies that focus on finding fuzzy association rules from precise and certain data. Unfortunately, real-world data tends to be uncertain due to human errors, instrument errors, recording errors, and so on. Therefore, a question arising immediately is how we can mine fuzzy association rules from uncertain data. To this end, this paper proposes a representation scheme to represent uncertain data. This representation is based on possibility distributions because the possibility theory establishes a close connection between the concepts of similarity and uncertainty, providing an excellent framework for handling uncertain data. Then, we develop an algorithm to mine fuzzy association rules from uncertain data represented by possibility distributions. Experimental results from the survey data show that the proposed approach can discover interesting and valuable patterns with high certainty.     

Mining fuzzy association rules for classification problems

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems.     

Mining fuzzy association rules from low-quality data

Data mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications have a certain degree of imprecision. In this paper we address this problem, and propose a new data-mining algorithm for extracting interesting knowledge from databases with imprecise data. The proposed algorithm integrates imprecise data concepts and the fuzzy apriori mining algorithm to find interesting fuzzy association rules in given databases. Experiments for diagnosing dyslexia in early childhood were made to verify the performance of the proposed algorithm.     

Dynamic scene understanding using temporal association rules

The basic goal of scene understanding is to organize the video into sets of events and to find the associated temporal dependencies. Such systems aim to automatically interpret activities in the scene, as well as detect unusual events that could be of particular interest, such as traffic violations and unauthorized entry. The objective of this work, therefore, is to learn behaviors of multi-agent actions and interactions in a semi-supervised manner. Using tracked object trajectories, we organize similar motion trajectories into clusters using the spectral clustering technique. This set of clusters depicts the different paths/routes, i.e., the distinct events taking place at various locations in the scene. A temporal mining algorithm is used to mine interval-based frequent temporal patterns occurring in the scene. A temporal pattern indicates a set of events that are linked based on their relationship with other events in the set, and we use Allen's interval-based temporal logic to describe these relations. The resulting frequent patterns are used to generate temporal association rules, which convey the semantic information contained in the scene. Our overall aim is to generate rules that govern the dynamics of the scene and perform anomaly detection. We apply the proposed approach on two publicly available complex traffic datasets and demonstrate considerable improvements over the existing techniques.     

基于日历的时序关联规则挖掘算法

以日历格作为框架来研究时序关联规则,提出了一个有效的挖掘算法。在用户指定的日历模式下,首先通过一次扫描产生所有的频繁2项集及相应的1*日历模式,在此基础上产生k*日历模式,并利用聚集性质产生候选K项集及相应的日历模式,最后扫描事务数据库产生所有的频繁项集及其日历模式。实验证明,该算法具有较好的性能。     

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).     

一种挖掘模糊相似关联规则的新方法

提出了一种基于自组织特征映射(SOFM)网络的自动确定样本数据隶属度函数的新方法,并在此基础上根据相似性的概念,给出了相似度的计算公式,结合Apriori算法,提出了一种挖掘模糊相似关联规则的新算法。与现有的同类算法相比,现有的方法均需人为地确定隶属度函数,带有一定的主观性,尤其当数据结构较复杂时,隶属度函数难以确定;该算法克服了这一缺点,同时减少了冗余规则。     

Discovery of unapparent association rules based on extracted probability

Association rule mining is an important task in data mining. However, not all of the generated rules are interesting, and some unapparent rules may be ignored. We have introduced an “extracted probability” measure in this article. Using this measure, 3 models are presented to modify the confidence of rules. An efficient method based on the support-confidence framework is then developed to generate rules of interest. The adult dataset from the UCI machine learning repository and a database of occupational accidents are analyzed in this article. The analysis reveals that the proposed methods can effectively generate interesting rules from a variety of association rules.     

基于模糊聚类的模糊关联规则在流程企业中的应用

采用数据挖掘中的模糊聚类分析了流程企业中历史数据的区间值,然后用模糊关联规则挖掘出有用的规则。首先阐述了模糊聚类的RFCM算法和关联规则的Apriori算法的内容,分析了实现模糊关联规则的Fuzzy_ClustApriori算法流程,并用RFCM算法对实际数据进行分析,得到不同类别的模糊数。根据Fuzzy_ClustApriori算法的步骤对模糊化的参数点进行处理,得到了有价值的模糊规则,为流程企业的生产优化提供了理论依据。     

Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection

Lee, Stolfo, and Mok 1 previously reported the use of association rules and frequency episodes for mining audit data to gain knowledge for intrusion detection. The integration of association rules and frequency episodes with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in intrusion detection and security itself is fuzzy. We present a modification of a previously reported algorithm for mining fuzzy association rules, define the concept of fuzzy frequency episodes, and present an original algorithm for mining fuzzy frequency episodes. We add a normalization step to the procedure for mining fuzzy association rules in order to prevent one data instance from contributing more than others. We also modify the procedure for mining frequency episodes to learn fuzzy frequency episodes. Experimental results show the utility of fuzzy association rules and fuzzy frequency episodes for intrusion detection. © 2000 John Wiley & Sons, Inc.