基于公共安全智能体的网络体系架构研究

城市智能体的发展对网络体系提出了新的要求.公共安全智能体作为城市智能体的一个重要分支也对网络有了更进一步的需求,需要网络能更主动、快速和灵活地支撑其发展,需要网络朝着更加智能化的方向发展.基于此,文章对基于公共安全智能体的人工智能(AI)网络体系架构进行了研究,在此基础上给出了公共安全智能体的平台框架和基于公共安全智能...     

基于Agent的空间数据挖掘研究

提出一种基于Agent理论的空间数据挖掘体系结构,并分析了其工作原理。进一步根据Agent的特点,设计了空间数据挖掘智能体的模板,并在其基础上,对农业空间数据实现高效的空间数据挖掘。     

人工智能在计算机视觉和网络安全中的应用

阐述人工智能的发展过程，人工智能在计算机视觉和网络安全中的应用，对网络中的访问数据进行甄别、专家系统、数据挖掘、智能流量监测、有效识别复杂的动态图像，保障网络的可靠与安全。     

网络教学平台下的数据挖掘技术探讨

在网络教学平台下,应用数据挖掘技术,有助于对网络教学平台上海量数据信息进行深入挖掘,并在此基础上设计更有效的功能和内容,来有效满足用户自主应用学习的需求,从而提高网络教学平台的应用价值。本文对网络教学平台下的数据挖掘技术展开探讨。     

基于云计算的数据挖掘算法研究

随着网络信息技术的快速发展,面对网络中海量数据的挖掘存在着计算能力和存储空间两方面的限制,云计算技术可以有效地解决数据挖掘中出现的这些问题。本文介绍了云计算的关键技术,数据挖掘算法的分类以及云计算平台下的数据挖掘算法。     

智能电网应用的电力大数据关键技术研究

大数据挖掘本质上以智能电网大数据平台为基础的,因此必须重视智能电网、云计算、大数据的相关研究。本文结合智能电网大数据关键技术,给出了大数据平台架构和业务应用架构。     

大数据分析技术在通信网络运维中的运用及分析

随着网络规模越来越大,通信网络的故障在不断增多,业界急需大数据、云计算等技术在通信网络运维中的应用。文章首先分析了大数据相关研究的背景,其次剖析了目前通信运维故障处理的方法及其短板,最后结合大数据分析技术的特点和通信网络故障的现状,文章设计了通信网络大数据综合故障定位分析平台框架,进行了分析平台的功能设计,平台实现了通信网络拥塞度大数据分析、预测通信信道分配、大数据挖掘故障定位和基于大数据挖掘故障分析等功能,较好地达到了网络运维的效果,希望能给大数据技术在通信领域应用的研究学者提供参考。     

基于信令检测平台对交通智能数据的挖掘应用

统一信令监测平台向整合统一方向发展,信令分析向海量数据深度挖掘发展。本文分析了统一信令平台海量数据挖掘应用。     

基于 Agent 和聚类分析的网络入侵检测研究

为了解决现有的入侵检测系统响应网络环境慢、需要较多人工干预的局限,采用智能体技术和聚类分析设计一种新的网络入侵检测方法.使用智能体技术实现入侵检测系统的分布式设计,利用分层的控制智能体实现入侵检测系统的自主控制,同时使用基于模糊 C 均值算法的数据挖掘技术对网络数据进行检测分析,并利用加权算法对模糊 C 均值算法进行改进,提高系统的检测能力.结果表明,该系统能够减少人工干预,对网络环境响应较快,入侵检测性能也得到了提高.     

基于云计算的数据挖掘平台架构及其关键技术

云计算,作为一种当代新兴的网络技术,支持对海量信息的高效处理。同时,基于云计算的数据挖掘技术也成为了当今研究的热点,它突破了传统数据挖掘系统的性能瓶颈。本文对基于云计算的数据挖掘平台的相关概念进行了一些解释,并对平台的架构以及关键技术做了分析。     

一种网络化数据挖掘方法研究

传统数据挖掘方法一般挖掘和发现其中的广义知识、关联知识、分类知识和预测知识等。随着对WS小世界网络和BA无尺度网络等复杂网络性能研究的不断深入．复杂网络结构中本身蕴含的知识是一类新型的重要知识，文章研究了一种网络化数据挖掘（Networked Data Mining），将网络拓扑作为一种知识表示方式，将大规模实际数据对象及其关系抽象为网络拓扑的形式，采用复杂网络的理论和方法进行分析和挖掘，发现蕴涵在其中的、反映事物间联系规律的知识和信息。实验表明，该方法能够有效挖掘复杂网络拓扑中的骨干节点和社区结构。     

基于数据融合和数据挖掘技术的网络取证体系的研究

数据融合技术能利用不同传感数据的冗余信息实现互补以提高信息处理的正确性。数据挖掘技术能从大量数据中挖掘和发现有价值和隐含的知识,用于建模和优化。在分析了网络取证在网络安全领域的重要作用的基础上,结合数据融合和数据挖掘技术对网络取证系统进行了研究,并阐述其功能和组成、数据融合和推理决策的过程。     

Exploiting data preparation to enhance mining and knowledgediscovery

One of the major obstacles to using organizational data for mining and knowledge discovery is that, in most cases, it is not amenable for mining in its natural form. Using a data set from a large tertiary-care hospital, we provide strong empirical evidence that data enhancement by the introduction of new attributes, along with judicious aggregation of existing attributes, results in higher-quality knowledge discovery. Interestingly, we also found that there is a differential impact of data set enhancements on the performance of different data mining algorithms. We define and use several measures, including entropy, rule complexity and resonance, to evaluate the quality and usefulness of the knowledge discovered     

An integrative and interactive framework for improving biomedical pattern discovery and visualization

Recent progress in medical sciences has led to an explosive growth of data. Due to its inherent complexity and diversity, mining such volumes of data to extract relevant knowledge represents an enormous challenge and opportunity. Interactive pattern discovery and visualization systems for biomedical data mining have received relatively little attention. Emphasis has been traditionally placed on automation and supervised classification problems. Based on self-adaptive neural networks and pattern-validation statistical tools, this paper presents a user-friendly platform to support biomedical pattern discovery and visualization. It has been tested on several types of biomedical data, such as dermatology and cardiology data sets. The results indicate that in comparison to traditional techniques, such as Kohonen Maps, this platform may significantly improve the effectiveness and efficiency of pattern discovery and classification tasks, including problems described by several classes. Furthermore, this study shows how the combination of graphical and statistical tools may make these patterns more meaningful.     

Improving Pattern Discovery and Visualization of SAGE Data Through Poisson-Based Self-Adaptive Neural Networks

Serial analysis of gene expression (SAGE) allows a detailed, simultaneous analysis of thousands of genes without the need for prior, complete gene sequence information. However, due to its inherent complexity and the lack of complete structural and function knowledge, mining vast collections of SAGE data to extract useful knowledge poses great challenges to traditional analytical techniques. Moreover, SAGE data are characterized by a specific statistical model that has not been incorporated into traditional data analysis techniques. The analysis of SAGE data requires advanced, intelligent computational techniques, which consider the underlying biology and the statistical nature of SAGE data. By addressing the statistical properties demonstrated by SAGE data, this paper presents a new self-adaptive neural network, Poisson-based growing self-organizing map (PGSOM), which implements novel weight adaptation and neuron growing strategies. An empirical study of key dynamic mechanisms of PGSOM is presented. It was tested on three datasets, including synthetic and experimental SAGE data. The results indicate that, in comparison to traditional techniques, the PGSOM offers significant advantages in the context of pattern discovery and visualization in SAGE data. The pattern discovery and visualization platform discussed in this paper can be applied to other problem domains where the data are better approximated by a Poisson distribution.     

数据挖掘技术的方法和最新进展

随着大型数据库的不断涌现，不缺数据缺知识的矛盾日益突出。如何从浩如烟海的数据中发现隐藏的知识，创造更大的效益是一个迫切需要解决的课题。因此，知识发现和数据挖掘应运而生，成为一个新的研究领域。在此，介绍了数据仓库、知识发现和数据挖掘的基本概念，重点论述了数据挖掘技术的方法，并指出了研究方向。     

短信舆情信息处理平台及关键技术

分析了短信舆情信息处理平台的结构设计和系统处理流程等方面的问题,开发了完整短信舆情监管系统,介绍了平台中的短信信息采集模块、短信信息处理模块、热点信息发现模块的主要功能,提出了一种适合短信数据的数据采集方法及热点发现方法。系统运行结果表明,系统能有效保证短信网络内容安全,准确率较高。     

A hybrid association rule mining approach for characterizing network traffic behaviour

Understanding network traffic behaviour is crucial for managing and securing computer networks. One important technique is to mine frequent patterns or association rules from analysed traffic data. On the one hand, association rule mining usually generates a huge number of patterns and rules, many of them meaningless or user‐unwanted; on the other hand, association rule mining can miss some necessary knowledge if it does not consider the hierarchy relationships in the network traffic data. Aiming to address such issues, this paper proposes a hybrid association rule mining method for characterizing network traffic behaviour. Rather than frequent patterns, the proposed method generates non‐similar closed frequent patterns from network traffic data, which can significantly reduce the number of patterns. This method also proposes to derive new attributes from the original data to discover novel knowledge according to hierarchy relationships in network traffic data and user interests. Experiments performed on real network traffic data show that the proposed method is promising and can be used in real applications. Copyright © 2013 John Wiley & Sons, Ltd.     

数据挖掘算法研究

数据挖掘技术是一个不断发现知识的过程。阐述了目前三种流行的数据挖掘算法的理论模型及算法思想,并从算法时间复杂度、依赖条件和误差估计等方面进行了比较分析,说明了不同算法的优势和不足。指出了不同算法的应用前景,提出了一种将不同算法结合以减少误差率,提高算法效率的思路。