Web使用挖掘的应用研究

Some effective and efficient knowledge patterns will be gained through searching, integrating, mining and analyzing on the Web. These useful knowledge patterns can help us to build so efficient Web site that WWW can ser-vice people well. In this paper we point out Web Usage Mining process influenced by Web site structure and content,and introduce the application of Web Usage mining in E-commerce. In the end a example of Web Usage Mining is given.     

Incremental mining of the schema of semistructured data

Semistructued data are specified in lack of any fixed and rigid schema,even though typically some implicit structure appears in the data.The huge amounts of on-line applications make it important and imperative to mine the schema of semistructured data ,both for the users(e.g.,to gather useful information and facilitate querying)and for the systems (e.g.,to optimize access).The critical problem is to discover the hidden structure in the semistructured data.Current methods in extracting Web data structure are either in a general way independent of application background,or bound in some concrete environment such as HTML,XML etc.But both face the burden of expensive cost and difficulty in keeping along with the frequent and complicated variances of Web data.In this paper,the problem of incremental mining of schema for semistructured data after the update of the raw data is discusses.An algorithm for incrementally mining the schema of semistructured data is provided,and some experimental results are also given,which show that incremental mining for semistructured data is more efficient than non-incremental mining.     

Mining Cross-Transaction Web Usage Patterns

Web Usage Mining is the application of data mining techniques to large web log databases in order to extract usage patterns. However, most of the previous studies on usage patterns discovery just focus on mining intra-transaction associations, i.e., the associations among items within the same user＇s transactions, m cross-transaction association rule describes the association relationships among different users＇ transactions. In this paper, the closure property of frequent itemsets, which can determine the complete set of all frequent items exactly and is usually much smaller than the latter, is used to mine cross-transaction association rules from web log databases. We give the basic notion of frequent cross-transaction closed itemsets and prove the related necessary theories. And an efficient algorithm, i.e. MFCCPS（Mining Frequent Cross-Transaction Closed Pageviews Sets）, is designed and implemented. At last, an extensive experimental result on two synthetic datasets shows that our approach outperforms previous methods.     

The Data Warehouse Model Based on Web Service Technology

This paper analyses the advantages of comhining Web service and data warehouse, then introduces a distributed data warehouse model based on Web service. It also introduces some features of Web services and data warehouse, Web service is a bridge, which can link different models, different operation systems and different program languages. With the development and application of Web service technology, the current operation of design and application will shift to develop and make use of Web service. Web service will matke ＂software as service＂ come true and finally let people think software is of great value, and its value can be embodied and transferred on the web. As a decision supporting system, data warehouse provides a solid plattbrm formed by current data and past data. Using this platform, companies can make a series of commerce analysis. This work combines the Web service and data warehouse, extends data warehouse＇s network ability. Then, Companies Corporation and individual can obtain information conveniently.     

IP网络计费管理研究

Accounting Management is probably the least developed IP network management application. Now it is paid more attention than before. Accounting is relatively a unattached and typical application compare to other IP network management applications. First , the non-technical factors are described. And then, the accounting objects and accounting policy are researched and described formally. The CORBA-based accounting architecture is presented.The architecture has three layers. First layer is the Web interface used as the users' interface. Java Applet is used to pass some active data. HTTP Server and CORBAAcct Server locate in the second layer. The database server is also located in this layer. The CORBAAcct Server defined by some CORBA objects accomplishes the accounting. The third layer is the device Agent on which the raw accounting data are introduced. The client communicates with the CORBAAcct Server by IIOP and the communication between CORBAAcct Server and device Agent is based on network management orotocol.     

StarBus+: Distributed Object Middleware Practice for Internet Computing

In the past decade, the booming of Internet challenges the middleware in three aspects: quality of service, balance of changes and stabilization, and across-Internet integration. This paper presents our work on distributed object computing middleware technology for these challenges, as well as the research and development on StarBus , which is a CORBA standard-compliant middleware suite with the features such as object request broker supporting multi-* quality of service, component model, and integration with Web Service. This paper comprehensively presents the design and characteristics of StarBus , and demonstrates how StarBus is enhanced to address the challenges of Internet computing through three case studies: inter-enterprise integration over Internet, application evolution through dynamic reconfiguration, and great massive information system building. The paper also suggests some research directions which are important for Internet computing.     

A new algorithm for fast mining frequent itemsets using N-lists

Mining frequent itemsets has emerged as a fundamental problem in data mining and plays an essential role in many important data mining tasks.In this paper,we propose a novel vertical data representation called N-list,which originates from an FP-tree-like coding prefix tree called PPC-tree that stores crucial information about frequent itemsets.Based on the N-list data structure,we develop an efficient mining algorithm,PrePost,for mining all frequent itemsets.Efficiency of PrePost is achieved by the following three reasons.First,N-list is compact since transactions with common prefixes share the same nodes of the PPC-tree.Second,the counting of itemsets’ supports is transformed into the intersection of N-lists and the complexity of intersecting two N-lists can be reduced to O(m + n) by an efficient strategy,where m and n are the cardinalities of the two N-lists respectively.Third,PrePost can directly find frequent itemsets without generating candidate itemsets in some cases by making use of the single path property of N-list.We have experimentally evaluated PrePost against four state-of-the-art algorithms for mining frequent itemsets on a variety of real and synthetic datasets.The experimental results show that the PrePost algorithm is the fastest in most cases.Even though the algorithm consumes more memory when the datasets are sparse,it is still the fastest one.     

An Overview of Data Mining and Knowledge Discovery

With massive amounts of data stored in databases,mining information and knowledge in databases has become an important issue in recent research.Researchers in many different fields have shown great interest in date mining and knowledge discovery in databases.Several emerging applications in information providing services,such as data warehousing and on-line services over the Internet,also call for various data mining and knowledge discovery tchniques to understand used behavior better,to improve the service provided,and to increase the business opportunities.In response to such a demand,this article is to provide a comprehensive survey on the data mining and knowledge discorvery techniques developed recently,and introduce some real application systems as well.In conclusion,this article also lists some problems and challenges for further research.     

A semi-random multiple decision-tree algorithm for mining data streams

Mining with streaming data is a hot topic in data mining. When performing classification on data streams, traditional classification algorithms based on decision trees, such as ID3 and C4.5, have a relatively poor efficiency in both time and space due to the characteristics of streaming data. There are some advantages in time and space when using random decision trees. An incremental algorithm for mining data streams, SRMTDS （Semi-Random Multiple decision Trees for Data Streams）, based on random decision trees is proposed in this paper. SRMTDS uses the inequality of Hoeffding bounds to choose the minimum number of split-examples, a heuristic method to compute the information gain for obtaining the split thresholds of numerical attributes, and a Naive Bayes classifier to estimate the class labels of tree leaves. Our extensive experimental study shows that SRMTDS has an improved performance in time, space, accuracy and the anti-noise capability in comparison with VFDTc, a state-of-the-art decision-tree algorithm for classifying data streams.     

Data-driven synthesis of multiple recommendation patterns to create situational Web mashups

As a typical situational application,Web mashup reflects and accommodates some key features of Internetware paradigm.Mashup provides a development fashion that integrates data,computation and UI elements from multiple resources into a single Web application,and promises the quick rollout of creating potential new functionalities opportunistically.This paper focuses on the problem of recommending useful suggestions for developing data-driven mashups by synthesis of multiple patterns.We present a rapid and intuitive system called iMashupAdvisor,for aiding mashup development based on a novel automated suggestion mechanism.The key observation guiding the development of iMashupAdvisor is that mashups developed by different users might share some common patterns,for instance,selecting similar mashup components for similar goals,and gluing them in a similar manner.Such patterns could reside in multiple sources,e.g.,the data dependency between mashup components,the interaction between users and mashup components,or the collective intelligence from existing applications created and maintained by programmers,etc.iMashupAdvisor leverages the synthesis of these patterns to recommend useful suggestions for a partial mashup,such as the missing components,connections between them,or potentially relevant options,to assist mashup completion.This paper presents the data model and ranking metrics of the synthesis process,and introduces efficient algorithms for the retrieval of recommendations.We also experimentally demonstrate the efficiency of our approach for benefiting the proposed rapid mashup development.     

Web使用挖掘技术分析

介绍了一种Web挖掘的分类，包括Web内容挖掘、Web结构挖掘和Web使用挖掘。讨论了Web使用挖掘过程的三个步骤，即数据获取与数据预处理、模式发现和模式分析，详细分析了每一个步骤中所使用的技术。指出了目前Web使用挖掘研究存在的不足，给出了Web使用挖掘未来的研究方向。     

Web数据挖掘

Web Mining is an important branch in Data Mining.It attracts more research interest for rapidly developing Internet. Web Mining includes(1)Web Content Mining;(g)Web Usage Mining;(3) Web structure Mining.In this paper we define Web Mining and present an overview of the various research issues,techniques and development efforts.     

Web模糊聚类方法及其应用

本文提出了Web模糊聚类的概念,给出了Web模糊聚美的过程模型WFCM并进行了详细阐述,沦述了Web模糊聚类在Web访问信息挖掘中,尤其是在Web用户聚类和Web页面聚类方面的应用．最后用实例证明了在Web页面聚类中使用Web模糊聚类的可行性。     

基于web挖掘的用户服务研究

数据丰富而知识贫乏导致了知识发现和数据挖掘领域的出现。基于Web的数据挖掘，是从Web海量的数据中自动、智能地抽取隐藏于这些数据中的知识，分析了Web挖掘技术的概念、特点、技术等。根据Web数据挖掘最流行的分类，可以分为Web内容挖掘、Web结构挖掘和Web使用记录挖掘。其中Web使用挖掘就是运用数据挖掘的思想来对服务器日志进行分析处理。该文根据Web数据挖掘的最近研究状况，主要论述了一个更新的频繁路径集的挖掘浏览模式在Web用户个性化服务中的应用，同时，还对发现的知识讨论了其在在线服务中的应用并给出了相应算法。     

集成Web使用挖掘和内容挖掘的用户浏览兴趣迁移挖掘算法

提出了一种集成Web使用挖掘和内容挖掘的用户浏览兴趣迁移模式的模型和算法。介绍了Web页面及其聚类。通过替代用户事务中的页面为相应聚类的方法得到用户浏览兴趣序列。从用户浏览兴趣序列中得到用户浏览兴趣迁移模式。该模型对于网络管理者理解用户的行为特征和安排Web站点结构有较大的意义。     

用户访问模式挖掘中数据预处理问题的研究

首先给出了用户访问模式挖掘的概念,然后主要对用户访问模式挖掘中的数据预处理工作中碰到的一些问题及对这些问题的解决方法进行了较为详细的描述。     

一种N层体系结构下的Web挖掘应用

Web应用的深入使N层体系结构的系统逐渐被广泛使用，同时网上的海量信息也为Web挖掘提供了一个广阔的应用领域。本文针对在N层体系结构中应用Web挖掘技术进行了研究；包括在N层体系结构中实现网站用户访问分析、智能搜索引擎和个性化推荐等；对数据源的处理和转换、数据仓厍的建立和使用以及业务处理过程的改进等都进行了论述。     

利用语义进行Web结构挖掘

本文论述了利用语义挖掘Web结构、Web使用挖掘进行了基于语义挖掘方法的探讨,并对PageRank算法进行了分析,针对该算法的不足之处进行了改进。     

利用语义进行Web结构挖掘

本文论述了利用语义挖掘Web结构、Web使用挖掘进行了基于语义挖掘方法的探讨，并对PageRank算法进行了分析，针对该算法的不足之处进行了改进。