基于公理化模糊集语义图像层次关联规则分类器

为提高语义图像分类器性能,提出一种基于公理化模糊集的语义图像层次关联规则分类器。首先,为提高算法精度,在对图像数据集进行特征提取基础上,采用公理化理论（AFS）构建图像集模糊概念的AFS属性表达,提高图像集属性辨识度;其次,为提高算法计算效率,考虑采用层次结构关联规则,构建语义图像分类器,利用概念之间的本体信息,提高并行分类能力;最后,通过对算法参数及横向对比实验,显示所提算法具有较高的计算精度和计算效率。     

基于TD-FP-growth的模糊关联规则挖掘算法

提出一种基于TD_FP-growth的模糊关联规则挖掘算法.首先,使用3种t-模算子以及由其产生的蕴涵算子计算模糊频繁项的支持度和规则的蕴涵度,产生的关联规则能表示模糊项间的确定性和渐近性逻辑语义;然后,以事务的惟一标识为键值,散列存储每个事务相对FP-tree中每个结点所表示模糊项的隶属度,使TD-FP-growth适用于模糊频繁项的挖掘,并分析了算法的时间和空间复杂度;最后,实验结果表明该算法比基于apriori的模糊频繁项挖掘算法在时间方面更加有效.

Abstract：

An algorithm based on TD-FP-growth is proposed for mining fuzzy association rule, which uses three kinds of t-norm operator to calculate the support degree of fuzzy frequent items, and adopts corresponding implication operator to measure implication degree of fuzzy association rule.The association rule mined by the algorithm can express the logic semantic of graduality and certainty between fuzzy items.Each transaction's membership degree versus fuzzy item denoted by FP-tree's node is stored by hash technology, and each transaction's identifier is regarded as key value, which adapts TD-FP-growth to mine fuzzy frequent items.The time and space complexity of the algorithm are analyzed.The experimental results show that the algorithm is more effective than the fuzzy frequent item mining algorithm based on apriori in term of time.     

基于模糊分类关联规则的支持向量机分类器生成方法

为提高数据库分类系统的分类精度,提出一种新的分类方法。首先,利用模糊C-均值聚类算法对数据库中的连续属性进行离散化;然后,在此基础上提出一种改进的模糊关联算法挖掘分类关联规则;最后,通过计算规则和模式之间的兼容性指标来构造特征向量,构建支持向量机的分类器模型。实验结果表明,该方法具有较高的分类识别能力和分类效率。     

基于时间衰减模型的模糊会话关联规则挖掘算法

现有的关联规则挖掘算法没有考虑数据流中会话的非均匀分布特性和历史数据的作用,并且忽略了连续属性处理时的“尖锐边界”问题。针对这些问题,本文提出一种基于时间衰减模型的模糊会话关联规则挖掘算法。首先,针对数据流中会话的非均匀分布特性,基于时间片对会话进行划分,完整的保留了时间片内会话之间的相关性信息;然后,采用模糊集对会话的连续属性进行处理,增加了规则的兴趣度和可理解性;最后,在考虑历史数据作用和允许误差情况的基础上,基于时间衰减模型挖掘数据流中的临界频繁项集和模糊关联规则。实验结果表明,本文方法在提高时间效率、降低冗余率和增加规则兴趣度方面存在明显优势。     

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.

     

大型数据库的模糊关联规则挖掘算法研究

为解决传统关联规则挖掘算法对大规模连续数据库进行挖掘时所产生的信息损失和效率低下等问题,给出一种改进的模糊关联规则挖掘算法,称为F-ARMVLQD算法。该算法利用模糊均值聚类算法解决离散属性间隔之间出现"尖锐边界"的问题,同时算法引入有向无环图和字节向量用以提高频繁项目集的计算效率,并吸取分区算法的优势,解决对该数据库挖掘时磁盘操作频繁的问题,整个算法只需扫描两次数据库。实验结果表明,该算法比传统算法具有更高的执行效率。     

基于图像分层树的图像语义分割方法

针对传统语义分割模型缺乏空间结构信息,无法准确地描述对象轮廓的问题,提出了一种基于图像分层树的图像语义分割方法。分层树模型采用结构森林方法生成轮廓模型,为防止过度分割,运用超度量轮廓图算法得到多尺度轮廓图,然后利用支持向量机训练多尺度轮廓图生成图像分层树,通过随机森林精炼分层树,最终输出图像语义分割结果。在测试实验中,像素精确度达到82.1%,相比区域选择方法(Selecting Regions)提升了2.7%。并在较难区分的树和山脉的预测精确度上,相比层次标记方法(Stacked Labeling)分别提升了16%,25%,具有更高的稳定性。实验结果表明,在复杂的室外环境下,对图像语义分割的精确度、稳定性和速率均有明显改善。     

基于FP-Tree的约束关联规则挖掘算法

针对构建FP-Tree时存在的大量内存消耗问题,提出了CCFP(constraint clip FP-tree)算法,该算法利用有项和缺项约束对事务数据库进行修剪后构造简化的FP-Tree,经再一次扫描后得到关联规则.实验结果表明:该算法较一般的FP-Tree算法能节省大量的内存空间,同时,运行效率也略有提高.     

Product portfolio identification based on association rule mining

It has been well recognized that product portfolio planning has far-reaching impact on the company's business success in competition. In general, product portfolio planning involves two main stages, namely portfolio identification and portfolio evaluation and selection. The former aims to capture and understand customer needs effectively and accordingly to transform them into specifications of product offerings. The latter concerns how to determine an optimal configuration of these identified offerings with the objective of achieving best profit performance. Current research and industrial practice have mainly focused on the economic justification of a given product portfolio, whereas the portfolio identification issue has been received only limited attention. This article intends to develop explicit decision support to improve product portfolio identification by efficient knowledge discovery from past sales and product records. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with requirement analysis enacted among customers, marketing folks, and designers. An association rule mining system (ARMS) is proposed for effective product portfolio identification. Based on a scrutiny into the product definition process, the article studies the fundamental issues underlying product portfolio identification. The ARMS differentiates the customer needs from functional requirements involved in the respective customer and functional domains. Product portfolio identification entails the identification of functional requirement clusters in conjunction with the mappings from customer needs to these clusters. While clusters of functional requirements are identified based on fuzzy clustering analysis, the mapping mechanism between the customer and functional domains is incarnated in association rules. The ARMS architecture and implementation issues are discussed in detail. An application of the proposed methodology and system in a consumer electronics company to generate a vibration motor portfolio for mobile phones is also presented.     

A fuzzy coherent rule mining algorithm

In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose common-sense knowledge which may not be interesting in business point of view. In this paper, an algorithm for mining fuzzy coherent rules is proposed for overcoming those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy the four criteria or not. If yes, it is a fuzzy coherent rule. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.     

A stock market portfolio recommender system based on association rule mining

We propose a stock market portfolio recommender system based on association rule mining (ARM) that analyzes stock data and suggests a ranked basket of stocks. The objective of this recommender system is to support stock market traders, individual investors and fund managers in their decisions by suggesting investment in a group of equity stocks when strong evidence of possible profit from these transactions is available.Our system is different compared to existing systems because it finds the correlation between stocks and recommends a portfolio. Existing techniques recommend buying or selling a single stock and do not recommend a portfolio.We have used the support confidence framework for generating association rules. The use of traditional ARM is infeasible because the number of association rules is exponential and finding relevant rules from this set is difficult. Therefore ARM techniques have been augmented with domain specific techniques like formation of thematical sectors, use of cross-sector and intra-sector rules to overcome the disadvantages of traditional ARM.We have implemented novel methods like using fuzzy logic and the concept of time lags to generate datasets from actual data of stock prices.Thorough experimentation has been performed on a variety of datasets like the BSE-30 sensitive Index, the S&P CNX Nifty or NSE-50, S&P CNX-100 and DOW-30 Industrial Average. We have compared the returns of our recommender system with the returns obtained from the top-5 mutual funds in India. The results of our system have surpassed the results from the mutual funds for all the datasets.Our approach demonstrates the application of soft computing techniques like ARM and fuzzy classification in the design of recommender systems.     

基于映射位集合的遥感图像关联规则挖掘

提出MBSA算法,采用Java中的TreeMap的映射技术和压缩的BitSet来存储大量的布尔变量值,并且该算法只扫描一次事务数据库,用BitSet的逻辑“与”操作来代替数据库的扫描,有效提高了运行速度。将该算法应用到遥感图像挖掘中,提取遥感图像中红、绿、蓝波段与农作物产量之间的关联,为提高农作物产量提供有益的支持     

基于递阶遗传算法模糊加权神经网络的模糊规则自动获取

针对模糊规则的自动获取一直是模糊系统的一个瓶颈问题,提出一种基于递阶结构的混合编码遗传算法与进化规划相结合的模糊加权神经网络学习新算法,利用该算法同时优化模糊加权神经网络的结构和参数,最后说明了从网络中提取模糊规则的方法,从而自动获得最优的模糊规则。分析和实验结果表明,本文方法在规则提取和分类准确性等方面比其他方法更好。     

基于关联规则的应用层特征挖掘

对净荷检测识别技术中的特征码提取方法进行了分析和研究,发现该技术目前主要采取手动寻找特征码的方式,需要投入大量的人力及时间,实现非常困难.针对该问题,提出了一种利用关联规则挖掘技术从IP流量载荷中提取应用层特征码的方法.实验结果表明,该方法准确率和有效率都非常高,可满足实际网络应用中的需求.     

基于T统计量的一种改进关联规则挖掘方法*

数据挖掘的一个重要任务便是从数据库中挖掘出有趣的关联规则。传统的关联规则挖掘方法一般基于支持度-置信度体系,时常会挖掘出虚假规则或忽略掉有用的规则。针对这一问题,本文借鉴对照实验的思想,提出基于T统计量的关联规则挖掘方法,用显著度代替置信度,使挖掘出的规则更具有统计显著性。算例分析和数据实验表明,这种方法可以解决传统关联规则方法存在的上述问题,提高关联规则的有效性。     

Compact fuzzy association rule-based classifier

Classification is one of the most popular data mining techniques applied to many scientific and industrial problems. The efficiency of a classification model is evaluated by two parameters, namely the accuracy and the interpretability of the model. While most of the existing methods claim their accurate superiority over others, their models are usually complex and hardly understandable for the users. In this paper, we propose a novel classification model that is based on easily interpretable fuzzy association rules and fulfils both efficiency criteria. Since the accuracy of a classification model can be largely affected by the partitioning of numerical attributes, this paper discusses several fuzzy and crisp partitioning techniques. The proposed classification method is compared to 15 previously published association rule-based classifiers by testing them on five benchmark data sets. The results show that the fuzzy association rule-based classifier presented in this paper, offers a compact, understandable and accurate classification model.     

Using a fuzzy association rule mining approach to identify the financial data association

In the rapidly changing financial market, investors always have difficulty in deciding the right time to trade. In order to enhance investment profitability, investors desire a decision support system. The proposed artificial intelligence methodology provides investors with the ability to learn the association among different parameters. After the associations are extracted, investors can apply the rules in their decision support systems. In this work, the model is built with the ultimate goal of predicting the level of the Hang Seng Index in Hong Kong. The movement of Hang Seng Index, which is associated with other economics indices including the gross domestic product (GDP) index, the consumer price index (CPI), the interest rate, and the export value of goods from Hong Kong, is learnt by the proposed method. The case study shows that the proposed method is a feasible way to provide decision support for investors who may not be able to identify the hidden rules between the Hang Seng Index and other economics indices.     

Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map

Fuzzy cognitive maps (FCMs) are one of the representative techniques in developing scenarios that include future concepts and issues, as well as their causal relationships. The technique, initially dependent on deductive modeling of expert knowledge, suffered from inherent limitations of scope and subjectivity; though this lack has been partially addressed by the recent emergence of inductive modeling, the fact that inductive modeling uses a retrospective, historical data that often misses trend-breaking developments. Addressing this issue, the paper suggests the utilization of futuristic data, a collection of future-oriented opinions extracted from online communities of large participation, in scenario building. Because futuristic data is both large in scope and prospective in nature, we believe a methodology based on this particular data set addresses problems of subjectivity and myopia suffered by the previous modeling techniques. To this end, text mining (TM) and latent semantic analysis (LSA) algorithm are applied to extract scenario concepts from futuristic data in textual documents; and fuzzy association rule mining (FARM) technique is utilized to identify their causal weights based on if-then rules. To illustrate the utility of proposed approach, a case of electric vehicle is conducted. The suggested approach can improve the effectiveness and efficiency of scanning knowledge for scenario development.     

Improved fuzzy weighted-iterative association rule based ontology postprocessing in data mining for query recommendation applications

The usage of association rules is playing a vital role in the field of knowledge data discovery. Numerous rules have to be processed and plot based on the ranges on the schema. The step in this process depends on the user's queries. Previously, several projects have been proposed to reduce work and improve filtration processes. However, they have some limitations in preprocessing time and filtration rate. In this article, an improved fuzzy weighted-iterative concept is introduced to overcome the limitation based on the user request and visualization of discovering rules. The initial step includes the mix of client learning with posthandling to use the semantics. The above advance was trailed by surrounding rule schemas to fulfill and anticipate unpredictable guidelines dependent on client desires. Preparing the above developments can be imagined by the use of yet another clever method of study. Standards on guidelines are recognized by the average learning professionals.     

基于激励的关联规则的挖掘

基于支持度的关联规则挖掘算法无法找到那些非频繁但效用很高的项集,基于效用的关联规则会漏掉那些效用不高但发生比较频繁、支持度和效用值的积（激励）很大的项集。提出了基于激励的关联规则挖掘问题及一种自下而上的挖掘算法HM-miner。激励综合了支持度与效用的优点,能同时度量项集的统计重要性和语义重要性。HM-miner利用激励的上界特性进行减枝,能有效挖掘高激励项集。