Association rule mining in peer-to-peer systems

We extend the problem of association rule mining--a key data mining problem--to systems in which the database is partitioned among a very large number of computers that are dispersed over a wide area. Such computing systems include grid computing platforms, federated database systems, and peer-to-peer computing environments. The scale of these systems poses several difficulties, such as the impracticality of global communications and global synchronization, dynamic topology changes of the network, on-the-fly data updates, the need to share resources with other applications, and the frequent failure and recovery of resources. We present an algorithm by which every node in the system can reach the exact solution, as if it were given the combined database. The algorithm is entirely asynchronous, imposes very little communication overhead, transparently tolerates network topology changes and node failures, and quickly adjusts to changes in the data as they occur. Simulation of up to 10,000 nodes show that the algorithm is local: all rules, except for those whose confidence is about equal to the confidence threshold, are discovered using information gathered from a very small vicinity, whose size is independent of the size of the system.     

三峡库区岩性植被关联规则挖掘

三峡库区属于南方高植被覆盖区域,岩石上部覆盖着较厚的土壤和茂密的植被,因此岩性分析比较困难,尚无成熟的方法可循。三峡库区遥感岩性分析的关键在于分析表层植被与岩性的关系,寻找消除表层植被的影响直接提取岩性信息的方法。针对三峡库区这一地形复杂、地质灾害频繁、土壤植被发育的地区分析和挖掘出岩性和植被的关联规则;通过将遥感影像与地质图叠加,计算植被指数NDVI图像,在各地层内随机选点,分析各点的岩性与NDVI值的关系,基于概念格算法和规则提取,挖掘出三峡库区嘉陵江组二段T₁j²,嘉陵江组三段T₁j³,巴东组一段T₂b¹,巴东组二段T₂b²,大冶组T₁d等地层的岩性和植被的关联规则。     

三峡库区巴东段地质灾害因子关联规则挖掘

选择地质灾害较为发育的巴东县为研究区,并以该区灾害点为数据样本,利用GIS将灾害点与地层岩性、高程、坡度、坡向、水系组合、遥感影像土地利用分类结果等6个影响因子进行叠加分析,选取灾害点的灾害类型、灾害规模、灾害体的物质类型、高程差、水系岸别等5个属性与叠加分析结果利用Apriori算法进行关联规则挖掘,最后挖掘出灾害规模与水系组合间关系等单因素间关联以及不同灾害属性与各因子间的关系等多因素间关联。通过与前人的相关研究成果对比分析,证明得出的规则具有合理性并符合实际情况,可为地质灾害分析决策提供先验知识。     

关联规则在WAP增值业务日志挖掘中的应用

日志挖掘为WAP增值业务运营和策略调整提供了数据依据.介绍了WAP增值业务中日志预处理.引入关联规则的概念到WAP增值业务日志挖掘中,分析了经典数据挖掘Apfiori算法.从两方面做了改进:利用修剪技术,由一项频繁集生成二项候选集,减少大量二项候选集;用扫描内存代替扫描数据库,减少大量扫描时间.实验表明这两种改进方法能快速完成WAP增值业务中素材关联的挖掘.     

数据挖掘在胎儿心率上的规则预测研究

瞬时胎心率是监测胎儿健康状态的一种重要方式。当前,监控胎儿心率是重要而复杂的任务,正确的自动化分类和规则提取是非常必要的。医疗诊断自动化系统,不仅加强医疗保健,同时也可以降低成本。设计了一个有效挖掘规则,并根据给定的参数来预测胎儿的风险水平。采用C4.5、Classification and Regression Tree(CART)、随机森林分类器来进行系统比较。该系统的性能评价由分类精度、产生规则数量构成。实验结果表明,基于随机森林分类器的系统具有高精度(99.4%)的预测胎儿健康状态的潜力,同时,产生的规则数量精简且可供于医生决策。     

大型数据库中利用强化学习改进treap的关联规则挖掘算法

信息的爆炸式增长使数据挖掘分析过程更加困难,针对普通关联规则挖掘算法很难在短运行时间和低关联度的前提下完成大型数据库中变量关系的评估和发现的问题,提出利用强化学习算法改进treap的大型数据库关联规则挖掘算法。提出的算法首先计算数据库中每个变量的优先级;然后,在优先级模型中利用强化学习算法改进的build-treap程序构建treap数据结构;最后,通过遍历程序和generateRule程序完成数据库中所需的关系查找。在对提出的算法进行稳定性分析后进行了仿真验证实验,实验结果表明,提出的算法在其最次和最佳案例分析中分别能够完成O(n log n)次和O(n 2)次挖掘,能够在较短时间内完成低关联度的大型数据库中变量关系挖掘任务,相对于改进型Apriori算法和改进型FP生长算法有较大提升。     

Different orientations of males and females in computer-mediated negotiations

This paper examines the existence of gender differences in computer mediated (CM) negotiations where “gender differences” refers to the differential patterns of behavior of males and females proposed by Rubin and Brown (Rubin, J. Z., & Brown, B. R. (1975). Bargainers as individuals. In The social psychology of bargaining and negotiation (pp. 157–196). New York: Academic Press). Namely, males are more profit oriented and females are more relationship oriented. External manipulations encouraging cooperativeness with other negotiators either by profitable or social incentives were inserted in the negotiations performed within the Colored Trails (CT) game framework. The negotiators included 27 females and 33 males who negotiated in foursomes via computers. In the first study we focused on independent negotiators whose success was not crucially dependent on the other party. In the second study negotiators were dependent upon one another, encouraging integrative solutions. The findings reveal that the social incentive (team factor) positively affected the females’ cooperativeness in contrast to males who were slightly less cooperative. On the other hand, profitable incentive influenced the males’ cooperativeness level, while no change was shown by females, which is consistent with Rubin and Brown’s distinction. These tendencies were reduced when playing with a non-reciprocal simulated agent. The causes for gender differences in CM as well as in face-to-face (FTF) negotiations are discussed.     

Association rule mining using genetic programming to provide feedback to instructors from multiple‐choice quiz data

This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar‐guided genetic programming is described and compared with both classical and recent soft‐computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups.     

Association rule mining through the ant colony system for National Health Insurance Research Database in Taiwan

In the field of data mining, an important issue for association rules generation is frequent itemset discovery, which is the key factor in implementing association rule mining. Therefore, this study considers the user’s assigned constraints in the mining process. Constraint-based mining enables users to concentrate on mining itemsets that are interesting to themselves, which improves the efficiency of mining tasks. In addition, in the real world, users may prefer recording more than one attribute and setting multi-dimensional constraints. Thus, this study intends to solve the multi-dimensional constraints problem for association rules generation.The ant colony system (ACS) is one of the newest meta-heuristics for combinatorial optimization problems, and this study uses the ant colony system to mine a large database to find the association rules effectively. If this system can consider multi-dimensional constraints, the association rules will be generated more effectively. Therefore, this study proposes a novel approach of applying the ant colony system for extracting the association rules from the database. In addition, the multi-dimensional constraints are taken into account. The results using a real case, the National Health Insurance Research Database, show that the proposed method is able to provide more condensed rules than the Apriori method. The computational time is also reduced.     

The measurement of maximum anaerobic alactacid powder in males and females

This study investigated the external power output in kgm s_?1 and vertical velocity in m s_?1 attained by 24 female and 24 male subjects during the following stair run tests: 2 m run-up negotiating two steps at a time, 6 m run-up negotiating three steps at a time, 2 m run-up negotiating three steps at a time and 6 m run-up negotiating two steps at a time. The steps were approximately 16.5 cm in height. Two timers were connected to photoelectric beam circuits and switchmats which were placed on the 8th and 12th steps when the subjects ran up the steps two at a time and on the 3rd and 9th steps when they ran up the steps three at a time. The photoelectric beam circuit and switchmat data were analysed separately for each sex by a 3 (high, medium and low leg length) × 2 (2 and 6 m run-up) × 2 (2 and 3 steps at a time) ANOVA repeated measures factorial design.

In each of the eight analyses, significantly (p < 0 05) greater scores were attained with a 6 as compared to a 2 m run-up and with negotiating three as compared to two steps at a time. These main effects must be interpreted in conjunction with a significant run-up × steps interaction which indicated that the length of the run-up had no significant effect when two steps were negotiated at a time. However, increasing the length of the run-up resulted in a significant increase when three steps were negotiated at a time. There was a significant main effect for leg length with the external power output in kg m s^-1 for the males. Those subjects in the high group scored significantly greater than those in the low group. A similar, though non-significant trend (p < 007), was observed with females. Photoelectric beam circuits yielded significantly higher scores than switchmats.

Of the four stair run protocols investigated in this study, the highest scores occurred with a 6 mrun-up, negotiating three 16.5 cm steps at a time and placing photoelectric beam circuits connected to a timer on the 3rd and 9th steps.     

Risk of neck musculoskeletal disorders among males and females in lifting exertions

Work-related neck disorders are common among various occupational groups. Despite clear epidemiological evidence for the association of these disorders with forceful arm exertions, the effect of such exertions on the biomechanical behavior of the neck muscles is currently not well understood. In this study, the effect of lifting tasks on the biomechanical loading of neck muscles was investigated for males and females. Twenty-six participants (13 males and 13 females) performed bi-manual isometric lifting tasks at knuckle, elbow, shoulder, and overhead heights by exerting 25%, 50%, and 75% of their maximum strength. The activity of the cervical trapezius and sternocleidomastoid muscles was recorded bilaterally using surface electromyography. Higher activity of the cervical trapezius muscle (10% MVC–43% MVC) compared to the sternocleidomastoid muscle (4% MVC–18% MVC) was observed. Females tend to use the sternocleidomastoid muscle to a greater extent than males, whereas, higher cervical trapezius muscle activation was observed for males than females. The main effect of weight and height, and weight by height interaction on the activity of neck muscles was statistically significant (all p < 0.001). The results of this study demonstrate that the neck muscles play an active role during lifting activities and may influence development of musculoskeletal disorders due to resulting physiological changes.     

Risk of musculoskeletal disorders among females and males in repetitive/constrained work

This paper combines epidemiological data on musculoskeletal morbidity in 40 female and 15 male occupational groups (questionnaire data 3720 females, 1241 males, physical examination data 1762 females, 915 males) in order to calculate risk for neck and upper limb disorders in repetitive/constrained vs. varied/mobile work and further to compare prevalence among office, industrial and non-office/non-industrial settings, as well as among jobs within these. Further, the paper aims to compare the risk of musculoskeletal disorders from repetitive/constrained work between females and males. Prevalence ratios (PR) for repetitive/constrained vs. varied/mobile work were in neck/shoulders: 12-month complaints females 1.2, males 1.1, diagnoses at the physical examination 2.3 and 2.3. In elbows/hands PRs for complaints were 1.7 and 1.6, for diagnoses 3.0 and 3.4. Tension neck syndrome, cervicalgia, shoulder tendonitis, acromioclavicular syndrome, medial epicondylitis and carpal tunnel syndrome showed PRs > 2. In neck/shoulders PRs were similar across office, industrial and non-office/non-industrial settings, in elbows/hands, especially among males, somewhat higher in industrial work. There was a heterogeneity within the different settings (estimated by bootstrapping), indicating higher PRs for some groups. As in most studies, musculoskeletal disorders were more prevalent among females than among males. Interestingly, though, the PRs for repetitive/constrained work vs. varied/mobile were for most measures approximately the same for both genders. In conclusion, repetitive/constrained work showed elevated risks when compared to varied/mobile work in all settings. Females and males showed similar risk elevations. This article enables comparison of risk of musculoskeletal disorders among many different occupations in industrial, office and other settings, when using standardised case definitions. It confirms that repetitive/constrained work is harmful not only in industrial but also in office and non-office/non-industrial settings. The reported data can be used for comparison with future studies.     

基于GEP与小生境的关联规则挖掘的研究*

如何高效而正确地挖掘关联规则是数据挖掘领域的一个重要研究方向。在现有工作的基础上结合基因表达式编程进化算法提出了一种新的挖掘强关联规则的算法框架;提出并实现了基于小生境技术的基因表达式编程算法(niche-gene expression programming,NGEP)以用于挖掘关联规则。与同类算法的对比实验结果表明,NGEP不但能更快地收敛,还进一步提高了挖掘的正确率。     

滑坡稳定性与土地利用关联规则挖掘及应用 ——以三峡库区秭归到巴东段为例

三峡库区滑坡灾害广泛发育,其稳定性受土地利用变化等人类工程活动的影响。采用数据挖掘技术研究库区土地利用变化对滑坡稳定性的影响及其规律,利用三个时相的遥感影像得到实验区滑坡面上两个时段间的土地利用变化监测图,用Apriori算法挖掘出滑坡稳定性与土地利用变化类型之间的强规则,用马尔可夫链模型预测滑坡面上土地利用的变化趋势,将预测结果用于对滑坡稳定性发展的分析评估。通过实验分析,所采用的方法可用于预测滑坡稳定性变化趋势,为滑坡灾害的监测预警提供决策支持。     

Rule-based system to detect energy efficiency anomalies in smart buildings,a data mining approach

The rapidly growing world energy use already has concerns over the exhaustion of energy resources and heavy environmental impacts. As a result of these concerns, a trend of green and smart cities has been increasing. To respond to this increasing trend of smart cities with buildings every time more complex, in this paper we have proposed a new method to solve energy inefficiencies detection problem in smart buildings. This solution is based on a rule-based system developed through data mining techniques and applying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is also proposed to detect anomalies. The data mining system is developed through the knowledge extracted by a full set of building sensors. So, the results of this process provide a set of rules that are used as a part of a decision support system for the optimisation of energy consumption and the detection of anomalies in smart buildings.     

A framework for regional association rule mining and scoping in spatial datasets

The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. In this paper, we systematically study this problem and address the unique challenges of regional association mining and scoping: (1) region discovery: how to identify interesting regions from which novel and useful regional association rules can be extracted; (2) regional association rule scoping: how to determine the scope of regional association rules. We investigate the duality between regional association rules and regions where the associations are valid: interesting regions are identified to seek novel regional patterns, and a regional pattern has a scope of a set of regions in which the pattern is valid. In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results confirm and validate research results in the study of arsenic contamination, and our work leads to the discovery of novel findings to be further explored by domain scientists.     

An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering

Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through students’ usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.     

Empirical Bayes and Fully Bayes procedures to detect high-risk areas in disease mapping

Disease mapping studies have experienced an enormous development in the last twenty years. Both an Empirical Bayes (EB) and a Fully Bayes (FB) approach have been used for smoothing purposes. However, an excess of smoothing might hinder the detection of true high-risk areas. Identifying these extreme regions minimizing the misclassification of background or normal areas, and then, avoiding false alarms is crucial in epidemiology. Bayesian decision rules, based on the posterior distribution of the relative risks, have been investigated for this task, but no similar studies have been conducted under the EB approach. Within this framework, second order correct estimators of the MSE of the log-relative risk predictor can be used to build appropriate confidence intervals for the relative risks. Their ability to detect high-risk areas is investigated through a simulation study using the geographical structure of the well-known Scottish lip cancer data. Bayesian credibility intervals and decision rules, based on the posterior distribution of the relative risks, are also investigated to check if any of the approaches outperforms the others when classifying high-risk regions. The conclusion is that Bayesian decision rules, exploiting the posterior distribution of the relative risks, are more powerful to detect high-risk areas than EB confidence intervals, but no general rules can be defined as a global criterion to be routinely applied in every real setting.