基于Web Mining的推荐系统

推荐系统(Recommender System)被电子商务站点用来向顾客提供信息以帮助顾客选择产品,其基本思想是以统计结果或者顾客以前的行为记录为依据,推测顾客未来可能的行为并给出相应的推荐。本文对基于传统技术和Web mining技术的推荐系统进行了简要综述,同时描述了基于Web mining技术的推荐系统的工作流程,重点分析了应用于推荐系统的各种具体Web mining技术及其算法比较。     

Web数据挖掘中的数据预处理

Web数据挖掘是分析网络应用的主要手段，其数据源一般是网络服务器日志，然而日志记录的是杂乱的，不完整的，不准确的并且是非结构化的数据，必须进行数据预处理。文章将预处理过程分为3个阶段－数据清洗、区分使用者，会话识别，并提出了一个高效的Web数据挖掘预处理结构WLP和相应的算法。     

一种Web使用模式挖掘模型的设计

Web使用模式挖掘是对用户浏览Web后在服务器日志上所留信息的数据挖掘.介绍了挖掘中常用技术及流程,并提出一种Web使用模式挖掘体系结构,介绍了系统的工作原理,对系统设计中的数据清洗和会话识别等关键技术作了详细讨论.     

Incremental and interactive mining of web traversal patterns

Web mining involves the application of data mining techniques to large amounts of web-related data in order to improve web services. Web traversal pattern mining involves discovering users’ access patterns from web server access logs. This information can provide navigation suggestions for web users indicating appropriate actions that can be taken. However, web logs keep growing continuously, and some web logs may become out of date over time. The users’ behaviors may change as web logs are updated, or when the web site structure is changed. Additionally, it can be difficult to determine a perfect minimum support threshold during the data mining process to find interesting rules. Accordingly, we must constantly adjust the minimum support threshold until satisfactory data mining results can be found.The essence of incremental data mining and interactive data mining is the ability to use previous mining results in order to reduce unnecessary processes when web logs or web site structures are updated, or when the minimum support is changed. In this paper, we propose efficient incremental and interactive data mining algorithms to discover web traversal patterns that match users’ requirements. The experimental results show that our algorithms are more efficient than other comparable approaches.     

Integrating Web Prefetching and Caching Using Prediction Models

Web caching and prefetching have been studied in the past separately. In this paper, we present an integrated architecture for Web object caching and prefetching. Our goal is to design a prefetching system that can work with an existing Web caching system in a seamless manner. In this integrated architecture, a certain amount of caching space is reserved for prefetching. To empower the prefetching engine, a Web-object prediction model is built by mining the frequent paths from past Web log data. We show that the integrated architecture improves the performance over Web caching alone, and present our analysis on the tradeoff between the reduced latency and the potential increase in network load.     

Distributed data mining patterns and services: an architecture and experiments

Distributed data mining implements techniques for analyzing data on distributed computing systems by exploiting data distribution and parallel algorithms. The grid is a computing infrastructure for implementing distributed high‐performance applications and solving complex problems, offering effective support to the implementation and use of data mining and knowledge discovery systems. The Web Services Resource Framework has become the standard for the implementation of grid services and applications, and it can be exploited for developing high‐level services for distributed data mining applications. This paper describes how distributed data mining patterns, such as collective learning, ensemble learning, and meta‐learning models, can be implemented as Web Services Resource Framework mining services by exploiting the grid infrastructure. The goal of this work was to design a distributed architectural model that can be exploited for different distributed mining patterns deployed as grid services for the analysis of dispersed data sources. In order to validate such an approach, we presented also the implementation of two clustering algorithms on the developed architecture. In particular, the distributed k‐means and distributed expectation maximization were exploited as pilot examples to show the suitability of the implemented service‐oriented framework. An extensive evaluation of its performance was provided. Copyright © 2011 John Wiley & Sons, Ltd.     

分布式Web用户兴趣迁移模式挖掘研究

提出了一个结合Web文本挖掘的分布式Web使用挖掘模型DWLMST,以及基于该模型的局部浏览兴趣迁移模式更新算法LITP和全局浏览兴趣迁移模式更新算法GITP。利用页面聚类来表示用户兴趣。通过将用户事务中的页面替代为相应的聚类号来得到用户浏览兴趣序列。从用户浏览兴趣序列中分析得到用户浏览兴趣迁移模式。算法较好地解决了Web访问信息的异地存储、实时增长等因素给模式分析过程带来的困难,同时也提高了用户浏览兴趣表示的准确性。     

基于Web挖掘的个性化网络教育研究

介绍了Web挖掘在个性化网络教育中的作用,指出了Web挖掘的基本过程和关键技术,论述了应用Web挖掘进行个性化网络教育的体系结构及其系统实现。     

Web日志挖掘中的数据预处理的研究

为了更加合理地组织Ｗｅｂ服务器的结构,需要通过Ｗｅｂ日志挖掘分析用户的浏览模式,而Ｗｅｂ日志挖掘中的数据预处理工作关系到挖掘的质量。文章就此进行了深入的研究,提出一个包括数据净化、用户识别、会话识别和路径补充等过程的数据预处理模型,并通过一个实例具体介绍了各过程的主要任务。     

基于MFP算法的Web日志挖掘技术的研究

为了更加合理地组织Web服务器的结构,需要通过Web日志挖掘分析用户的访问模式.数据预处理和日志挖掘算法是Web日志挖掘中的关键技术.文章就此进行了深入的研究,在已知用户访问路径的基础上,提出一种基于MFP算法的日志挖掘算法,并结合实例具体介绍了该算法的执行过程.     

Web文本挖掘技术研究

作为从浩瀚的Ｗｅｂ信息资源中发现潜在的、有价值知识的一种有效技术,Ｗｅｂ挖掘正悄然兴起,倍受关注,目前,Ｗｅｂ挖掘的研究正处于发我统一的结论,需要国内外学者在理论上开展更多的讨论,同时,Ｗｅｂ挖掘系统的开发对其研究也将起到很大推进作用,首先探讨了Ｗｅｂ挖掘的有关理论,从Ｗｅｂ挖掘的定义、Ｗｅｂ挖掘与Ｗｅｂ信息检索的关系、Ｗｅｂ信息检索的关系、Ｗｅｂ挖掘任务的分类与功能等方面加以阐述,然后重点分析了     

Web数据挖掘与挖掘算法探讨

本文介绍了Web数据挖掘的概念及其分类,并对Web数据挖掘技术的研究进行概述。利用Apriori算法发现频繁集,找到页面间的关联规则。针对网页超链接结构的特点：一条超链接只能建立在两个网页上,发现频繁集只要找出所有2-项集即可,从而提出网页超链接挖掘的NApriori算法。NApriori算法显著提高了Apriori算法的效率。     

Web使用信息挖掘综述

Web使用信息挖掘可以帮助我们更好地理解Web和Web用户访问模式,这对于开发Web的最大经济潜力是非常关键的。一般来说,使用信息挖掘包含三个阶段：数据预处理,模式发现和模式分析。文章以这三个阶段为PWeb框架,分别介绍了数据预处理的技术与困难,Web使用信息挖掘中常用的方法和算法,以及主要应用。     

Web搜索中的数据挖掘技术研究

WWW已经成为世界上是大的分布式信息系统,如何快速有效地搜索用户所需的资源一直是研究热点。Web挖掘也已经成为数据挖掘中相对成熟的一个分支。本文针对Web资源搜索中利用的相关Web挖掘技术做一个综述。文章首先对目前流行的Web内容挖掘方面的常用技术进行了研究分析,然后着重研究了Web结构挖掘技术,介绍并评价了多种算法模型。接着介绍了用户使用的挖掘,并提出了Web内容挖掘技术,结构挖掘技术和用户使用挖掘相结合,应用于开发智能型搜索引擎的趋势。     

基于过滤器的Web访问模式挖掘

针对传统Web访问模式挖掘系统中用户识别和会话识别的复杂性和不准确性,该文提出了基于过滤器的Web访问模式挖掘系统。它能够准确地识别用户和会话,为挖掘算法提供优质的数据。给出了日志过滤器的实现和部署,提出了Web访问模式的挖掘算法。目前该方法已经广泛地应用于科学数据库系统中。     

Web日志挖掘探析

Web日志挖掘是数据挖掘领域中一个重要研究方向。文章对Web日志挖掘相关问题进行了探讨,分析了Web日志挖掘模式发现及其相关算法的不足,阐述了Web日志挖掘模式发现阶段增量更新的重要性。     

The Weka4WS framework for distributed data mining in service‐oriented Grids

The service‐oriented architecture paradigm can be exploited for the implementation of data and knowledge‐based applications in distributed environments. The Web services resource framework (WSRF) has recently emerged as the standard for the implementation of Grid services and applications. WSRF can be exploited for developing high‐level services for distributed data mining applications. This paper describes Weka4WS, a framework that extends the widely used open source Weka toolkit to support distributed data mining on WSRF‐enabled Grids. Weka4WS adopts the WSRF technology for running remote data mining algorithms and managing distributed computations. The Weka4WS user interface supports the execution of both local and remote data mining tasks. On every computing node, a WSRF‐compliant Web service is used to expose all the data mining algorithms provided by the Weka library. The paper describes the design and implementation of Weka4WS using the WSRF libraries and services provided by Globus Toolkit 4. A performance analysis of Weka4WS for executing distributed data mining tasks in different network scenarios is presented. Copyright © 2008 John Wiley & Sons, Ltd.     

基于本体的语义Web挖掘技术研究

为提高web服务的个性化系统服务水平,解决现有的Web服务缺少形式语义支撑,改善Web挖掘效果,提出了一个基于本体的语义Web挖掘模型,并对其基于本体论的语义和生成过程进行了详细论述,对提高Web服务个性化系统的效率和精度作了有益探讨。     

POLYPHONET: An advanced social network extraction system from the Web

Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, to detect groups of persons, and to obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.

Several studies have used search engines to extract social networks from the Web, but our research advances the following points: first, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social network mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Iterative Social Network Mining is proposed. It utilizes simple modules using Google and is characterized by scalability and relate–identify processes: identification of each entity and extraction of relations are repeated to obtain a more precise social network.     

一种基于Web服务的分布式数据挖掘体系结构

分布式数据挖掘是数据挖掘领域的一个新兴研究课题，而其主要问题是知识共享和软组件重用。结合Web服务技术的跨平台、统一数据表示格式以及可实现软组件重用和数据重用等优点，文中提出了一种基于Web服务的分布式数据挖掘体系，可实现分布式异构环境下的大容量数据的数据挖掘．旨在对异构数据库的数据挖掘进行一些有意义的探讨。