异构电子商务集成平台的本体模型构建

针对电子商务数据异构的特点,研究用本体技术实现异构电子商务的集成平台.通过不同电子商务本体特征的挖掘和抽取,提出异构电子商务集成平台的本体模型构建方法.实现电子商务本体协同的"即插即用"互操作集成模型.     

PPDM中面向k-匿名的MI Loss评估模型

隐私保护数据挖掘（PPDM）利用匿名化等方法使数据所有者在不泄露隐私信息的前提下,安全发布在数据挖掘中有效可用的数据集。k-匿名算法作为PPDM研究使用最广泛的算法之一,具有计算开销低、数据形变小、能抵御链接攻击等优点,但是在一些k-匿名算法研究中使用的数据可用性评估模型的权重设置不合理,导致算法选择的最优匿名数据集在后续的分类问题中分类准确率较低。提出一种使用互信息计算权重的互信息损失（MI Loss）评估模型。互信息反映变量间的关联关系,MI Loss评估模型根据准标识符和标签之间的互信息计算权重,并通过Loss公式得到各个准标识符的信息损失,将加权后的准标识符信息损失的和作为数据集的信息损失,以弥补评估模型的缺陷。实验结果证明,运用MI Loss评估模型指导k-匿名算法能够明显降低匿名数据集在后续分类中的可用性丢失,相较于Loss模型和Entropy Loss模型,该模型分类准确率提升了0.73%~3.00%。     

Reducing control selection errors associated with underground bolting equipment

Selecting the incorrect control during the operation of underground bolting and drilling equipment causes serious injuries. Shape coding and the layout of dual control banks are two aspects of control design which require further examination. The aims of this research were: (i) to determine whether arbitrary shape coding was effective in reducing selection error rates in a virtual analogy of roof-bolting; and (ii) to determine whether any advantages exist for mirror or place layouts for dual control situations in this situation. Two experiments were conducted to address these questions. No benefits of arbitrary shape coding were evident while control location remained constant. When control location was altered, shape coding did provide a significant reduction in selection error rate. No differences between mirror or place arrangements were detected and this question remains open.     

Sleep in a live-in mining operation: The influence of start times and restricted non-work activities

The amount of sleep obtained between shifts is influenced by numerous factors including the length of work and rest periods, the timing of the rest period relative to the endogenous circadian cycle and personal choices about the use of non-work time. The current study utilised a real-world live-in mining environment to examine the amount of sleep obtained when access to normal domestic, family and social activities was restricted. Participants were 29 mining operators (26 male, average age 37.4 ± 6.8 years) who recorded sleep, work and fatigue information and wore an activity monitor for a cycle of seven day shifts and seven night shifts (both 12 h) followed by either seven or fourteen days off. During the two weeks of work participants lived on-site. Total sleep time was significantly less (p < 0.01) while on-site on both day (6.1 ± 1.0 h) and night shifts (5.7 ± 1.5 h) than days off (7.4 ± 1.4 h). Further, night shift sleep was significantly shorter than day-shift sleep (p < 0.01). Assessment of subjective fatigue ratings showed that the sleep associated with both days off and night shifts had a greater recovery value than sleep associated with day shifts (p < 0.01). While on-site, participants obtained only 6 h of sleep indicating that the absence of competing domestic, family and social activities did not convert to more sleep. Factors including shift start times and circadian influences appear to have been more important.     

Truck scheduling at zero-inventory cross docking terminals

Handling freight at cross docking terminals constitutes a complex planning task which comprises several executive steps as shipments delivered by inbound trucks are to be unloaded, sorted according to their designated destinations, moved across the dock and finally loaded onto outbound trucks for an immediate delivery elsewhere in the distribution system. To enable an efficient synchronization of inbound and outbound flows, a careful planning of operations, e.g. by computerized scheduling procedures, becomes indispensable. This work treats a special truck scheduling problem arising in the (zero-inventory) cross docks of the food industry, where strict cooling requirements forbid an intermediate storage inside the terminal, so that all products are to be instantaneously loaded onto refrigerated outbound trucks. The problem is formalized such that different operational objectives, i.e. the flow time, processing time and tardiness of outbound trucks, are minimized. To solve the resulting truck scheduling problem suited exact and heuristic solution procedures are presented.     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

数据挖掘技术及Web应用

在计算机与信息技术普及的今天,人们的消费需求、行为和习惯都可以化为海量的用户数据。这样的数据挖掘成果,正逐步为公众所分享。     

Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to find those prototypes. In the MPSO algorithm each particle in a swarm represents a single prototype in the solution and it uses modified movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with artificial problems and with real benchmark problems and compared with several algorithms of the same family. Results show that the particles are able to recognize clusters, find decision boundaries and reach stable situations that also retain adaptation potential. The MPSO algorithm is able to improve the accuracy of 1-NN classifiers, obtains results comparable to the best among other classifiers, and improves the accuracy reported in literature for one of the problems.

A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan

Decision-making for the debris-flow management involves multiple decision-makers often with concerning geomorphological and hydraulic conditions. Spatial decision support systems (SDSS) can be developed to improve our understanding of the relations among the natural and socio-economic variables to the occurrence/non-occurrence samples of debris-flow. Accordingly, the goal of this study is to development a debris-flow decision support system to manage and monitor the debris-flows in Nan-Tou County, Taiwan. The present study, more specifically, combines a spatial information system with an advanced Data Mining technique to investigate the debris-flow problem. In the first stage, our spatial information system integrates remote sensing, DEM, and aerial photos as three different resources to generate our spatial database. Each of the geomorphological and hydraulic attributes are obtained automatically through our spatial database. Then, a Data Mining classifier (hybrid model of decision tree (D.T.) + support vector machine (S.V.M.)) will be used to analyze and resolve the classification of occurrence of debris-flow. The contribution of this study has found that watershed area and NDVI (Normalized Difference Vegetation Index) are the crucial factors governing debris-flow by means of decision tree analysis. Further, the performance of prediction accuracy on testing samples through support vector machine is 73% which could be helpful for us to have better understanding of debris-flow problem.     

基于元胞自动机的分布式数据挖掘分类器

针对常用分类方法分类精度较低和内存消耗较高的问题,设计一种基于多吸引子元胞自动机（MACA）的模式分类器tsPCM,把它应用于分布式数据挖掘。通过改变MACA的描述方法,用依赖串和依赖向量将分类过程设计成两阶段,用遗传算法优化设计。实验结果表明tsPCM具有较高的分类精度和较低的内存消耗,分类复杂度由O（n^3）降低到线性级O（n）,具有较好的应用价值。