Incrementally building frequent closed itemset lattice

A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world applications, where new transactions are constantly inserted into databases. To build an efficient storage structure for mining association rules, this study proposes a method for building the initial frequent closed itemset lattice from the original database. The lattice is updated when new transactions are inserted. The number of database rescans over the entire database is reduced in the maintenance process. The proposed algorithm is compared with building a lattice in batch mode to demonstrate the effectiveness of the proposed algorithm.     

Semantic weak signal tracing

The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organization’s decision making in strategic early warning environment.     

Web挖掘技术及其在网络教学评价中的应用

结合W eb日志挖掘技术和数据立方体技术应用于网络教学评价体系,以网络教学体系的W eb服务端和客户端的日志作为数据源,使用数据挖掘技术采集有用的数据进行分析抽取,建立数据库,再应用数据立方体技术、OLAP进行分析处理,得到所需的评价结果。结果表明,此算法可以客观的评价网络教学结果。     

破碎覆岩回采巷道临界宽度研究

为了有效控制破碎覆岩回采巷道变形,根据其赋存特征采用工字钢架棚支护技术,分析破碎覆岩普氏拱结构的形成原理和研究回采巷道临界宽度。针对破碎覆岩回采巷道具有围岩破碎、强度低和不具有可锚性等特点,采用普氏拱理论,计算回采巷道围岩压力;当棚梁工字钢最大正应力等于工字钢屈服极限时推导出破碎覆岩条件下回采巷道临界宽度计算公式。以106煤矿为工程实例进行分析,结果表明：破碎覆岩回采巷道的合理临界宽度为4.4m;增大支护强度和减小巷道高度可有效提高破碎覆岩回采巷道临界宽度;如果需要更大的巷道宽度,可通过降低棚间距或增大型钢型号方法实现。     

Credit rating with a monotonicity-constrained support vector machine model

Deciding whether borrowers can fulfill their obligations is a major issue for financial institutions, and while various credit rating models have been developed to help achieve this, they cannot reflect the domain knowledge of human experts. This paper proposes a new rating model based on a support vector machine with monotonicity constraints derived from the prior knowledge of financial experts. Experiments conducted on real-world data sets show that the proposed method, not only data driven but also domain knowledge oriented, can help correct the loss of monotonicity in data occurring during the collecting process, and performs better than the conventional counterpart.     

Exploring the Interactions of Storylines from Informative News Events

Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.     

极限训练机在矿山开采最大下沉值估算中的应用

矿山开采过程中采空区地表往往会发生形变,研究其最大下沉值对安全生产具有重要意义,现有的开采最大下沉估算方法还有提升的空间。基于岩移数据决策与极限训练机(Extreme learning machine,ELM)算法,提出了一种估计最大下沉的新方法。该方法将采厚、倾角、平均采深、走向长度、倾向长度和覆岩岩性确定为最大下沉值影响因素;应用以Sigmoid方程为核函数、隐含层神经元个数为114的ELM模型对最大下沉值进行了估算。通过案例分析,ELM模型得到了优于传统算法如CHAID、Boosted Tree、ANN、BPNN和SVM的RMSE、MAE、MAPE、最大残差及秩相关系数,故认为该模型是一种有效的矿山开采最大下沉估算方法。     

露天煤炭资源合理开发的优序决策

运用层次分析法建立递阶结构的露天煤田综合评价模型及评价指标体系,为露天煤田的合理开发提供了一种优序决策的方法。用该模型对七个露天煤田进行了综合评价,说明诙评价模型较为可靠和实用。     

矿山工程设计可靠性研究

本文讨论了矿山工程设计中地质储量、矿山工程建设、工艺系统、露天矿投资等的可靠性计算方法,据此可对矿山能否达到设计规模及预期效益作出评价,从而可采取相应对策。     

浅谈煤矿地质构造评价指标系统的建立与优选

煤炭资源是我国社会发展的重要能源之一,在煤炭资源开采过程中,必须对煤矿的地质结构进行认真分析。重点分析了煤矿地质结构与煤矿开采的关系,通过对煤矿地质结构的发展规律进行分析,建立了一套完善的煤矿地质构造评价指标系统,从而为煤矿资源的安全开采提供了保障。