Relational generative topographic mapping

The generative topographic mapping (GTM) has been proposed as a statistical model to represent high-dimensional data by a distribution induced by a sparse lattice of points in a low-dimensional latent space, such that visualization, compression, and data inspection become possible. The formulation in terms of a generative statistical model has the benefit that relevant parameters of the model can be determined automatically based on an expectation maximization scheme. Further, the model offers a large flexibility such as a direct out-of-sample extension and the possibility to obtain different degrees of granularity of the visualization without the need of additional training. Original GTM is restricted to Euclidean data points in a given Euclidean vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pairwise dissimilarities of data can be computed, i.e. the relations between data points are given rather than the data vectors themselves. We propose a method which extends the GTM to relational data and which allows us to achieve a sparse representation of data characterized by pairwise dissimilarities, in latent space. The method, relational GTM, is demonstrated on several benchmarks.     

Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.     

Learning automata-based solutions to the optimal web polling problem modelled as a nonlinear fractional knapsack problem

We consider the problem of polling web pages as a strategy for monitoring the world wide web. The problem consists of repeatedly polling a selection of web pages so that changes that occur over time are detected. In particular, we consider the case where we are constrained to poll a maximum number of web pages per unit of time, and this constraint is typically dictated by the governing communication bandwidth, and by the speed limitations associated with the processing. Since only a fraction of the web pages can be polled within a given unit of time, the issue at stake is one of determining which web pages are to be polled, and we attempt to do it in a manner that maximizes the number of changes detected. We solve the problem by first modelling it as a stochastic nonlinear fractional knapsack problem. We then present an online learning automata (LA) system, namely, the hierarchy of twofold resource allocation automata (H-TRAA), whose primitive component is a twofold resource allocation automaton (TRAA). Both the TRAA and the H-TRAA have been proven to be asymptotically optimal. Finally, we demonstrate empirically that the H-TRAA provides orders of magnitude faster convergence compared to the learning automata knapsack game (LAKG) which represents the state-of-the-art for this problem. Further, in contrast to the LAKG, the H-TRAA scales sub-linearly. Based on these results, we believe that the H-TRAA has also tremendous potential to handle demanding real-world applications, particularly those which deal with the world wide web.     

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Purpose

Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence.

Method

Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function.

Results

The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers.     

基于维基百科的未登录词译文挖掘

未登录词(out of vocabulary,OOV)的查询翻译是影响跨语言信息检索(cross-language information retrieval,CLIR)性能的关键因素之一.它根据维基百科(Wikipedia)的数据结构和语言特性,将译文环境划分为目标存在环境和目标缺失环境.针对目标缺失环境下的译文挖掘难点,它采用频度变化信息和邻接信息实现候选单元抽取,并建立基于频度-距离模型、表层匹配模板和摘要得分模型的混合译文挖掘策略.实验将基于搜索引擎的未登录词挖掘技术作为baseline,并采用TOP1进行评测.实验验证基于维基百科的混合译文挖掘方法可达到0.6822的译文正确率,相对baseline取得6.98%的改进.     

网格访问控制与授权模型的设计与实现

访问控制和资源授权是网格系统中资源与用户的关系策略的集合,分析了访问控制与资源授权的设计原则,提出了一种基于禁止表和允许表的网格用户访问控制层次式AB4L访问控制模型.给出了该模型的形式化定义,叙述了基于Postgres数据库的资源访问控制模型和授权的实现方法,并从完备性、可扩展性、自主控制和安全性方面对该模型进行了性...     

面向领域的关联规则评价方法及其应用

针对关联规则挖掘中,基于支持度-置信度框架的关联规则评价标准存在缺乏具体应用领域的分析,挖掘结果很难用于用户决策等问题,提出一种面向领域关联规则评价方法。该方法以领域知识为基准,发现满足技术兴趣度和商业兴趣度的规则,以国家住宅工程中心40个健康住宅试点项目的实际调查数据为例,进行试验和分析。在此基础上,设计并开发了居住健康领域挖掘系统,该系统采用多层次软件架构,包括知识库管理、挖掘数据选择、数据预处理、领域挖掘和结果评价等功能。实验结果和系统应用结果表明了面向领域关联规则评价方法的有效性。     

政务信息资源中心框架设计及关键技术研究

目前国内有关政务信息资源共享的研究及应用还停留在网络互联互通、信息资源整合、点对点数据交换阶段,并没有达到"协同工作"的要求.鉴于此,提出了政务信息资源中心的概念,实现政务信息资源的统一获取、统一管理、规则化和规模化服务.结合工作实践,对其组成、总体框架、技术架构进行了设计,并就实现时需要解决的关键技术进行了研究.     

综放采煤自动化煤岩识别传感器的研究

在实现综放采煤自动化的过程中,能准确检测区分出煤和岩石是实现无人作业工作面的难点.在高速数字信号处理器(DSP)平台上建立CCD数字处理系统,通过DSP对图像的离散脉冲噪声进行中值滤波,并实时计算出每一张图像的灰度直方图,提取煤炭和岩石的特征区别,从而实现对综放工作面的控制.通过Matlab仿真和实际测试结果表明:检测...     

Workflow simulation for operational decision support

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow’s design, but also use logged data describing the system’s observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.