多媒体数据挖掘技术研究

多媒体数据挖掘的系统原型,详细论述了多媒体图像数据挖掘和多媒体文本数据挖掘的过程和方法。     

源于信息挖掘的新型智能化决策支持系统

阐述了以结构化数据和复杂类型数据挖掘为主要内容的信息挖掘技术。采用7库(模型库、综合知识库、数据库、方法库、文本库、日志库、多媒体库)与双网(Internet、Intranet)相结合的体系结构,以信息挖掘技术为核心,提出源于信息挖掘的新型智能化决策支持系统(IDSSIM)。旨在解决决策支持系统对半结构化数据、非结构化数据的挖掘处理能力,使之适应目前信息源的多样型和动态变化性的特点,提供更加丰富的决策信息。     

多媒体数据挖掘技术研究

本文提出一种多媒体数据挖掘系统的一般结构和挖掘的过程,说明了不同类型多媒体数据挖掘的基本方法和技术,并对多媒体数据提出了阐述和展望。     

基于WEB的多媒体数据挖掘的研究

研究多媒体形式的数据的挖掘是目前一个很前沿的课题,它涉及到很多的领域包括网络、多媒体的技术、数据库以及关于知识的决策等。基于通常的一些数据库中所含的数据与多媒体的数据在一些特性上存在很多的不同点,也就使得在数据的挖掘上应用一些相对常规的方法没有办法实现特性上区别挖掘。这就需要研究出能够适用于挖掘多媒体数据的方法以及相应的技术。本研究介绍的是基于WEB技术实现多媒体数据的挖掘。     

辅助决策的新闻视频挖掘

新闻视频是一种丰富的信息源，对其进行有效的挖掘将有力地辅助决策分析。本文从多媒体信息系统的角度出发，深入思考了辅助决策的新闻视频的挖掘内容和挖掘方法，主要解决了以下两个问题：即挖掘什么和如何去挖的问题。指出辅助决策的新闻视频挖掘的内容应包括结构挖掘、语义事件挖掘、趋势挖掘、关联规则挖掘以及决策者兴趣点挖掘等等；并以一个具体的例子来说明新闻视频挖掘的各种方法，如结构的分割、语义事件的探测、新闻故事重要度的判定、专题的统计分析以及模式的可视化等等。本文较好地实现了新闻视频的内容分析与挖掘的结合，初步的实验证明了提出的新闻视频挖掘方法在辅助决策方面具有有效性。     

数据挖掘在数据分析中的应用

本文围绕着＂教师多媒体软件使用情况＂方面的四个问题,演示了数据挖掘的过程,重点阐述了如何利用数据挖掘工具,对挖掘结果的分类、测试检验、算法修改等操作,并将分类特征可视化,最终针对挖掘结果进行了分析,得出结论。对于数据挖掘在数据分析中的应用,本文做了很好的示范。     

频繁闭合项目集的并行挖掘算法研究

频繁项目集挖掘因其在数据挖掘领域中的基础地位和广泛应用备受学术界和产业界的关注，用挖掘频繁闭合项目集代替挖掘频繁项目集是近年来提出的一个重要策略。不同于以往提出的挖掘所有频繁项目集的并行算法，本文针对频繁闭合项目集的特性及并行挖掘的特点，给出了共享存储器模型上(Shared Memory)基于频繁模式树(FP-tree)的挖掘频繁闭合项目集的并行算法(FCIPM)思想，提出了频繁闭合项目集直接判断法，性能分析表明所提技术对算法的性能提高起到了关键作用。     

互联网上的数据挖掘

1 引言 Web挖掘是从WWW资源上抽取信息(或知识)的过程,它是将数据挖掘技术和理论应用于对WWW资源进行挖掘的一个新兴的研究领域。目前在该研究领域中,根据挖掘对象的不同大致可分为三个方面的挖掘研究:Web内容挖掘、Web结构挖掘、Web使用挖掘(图1)。     

带时间特征的序列模式挖掘算法TESP

引入序列模式时间特征的概念，并提出了一个带时间约束的序列模式挖掘算法，称做TESP(Time-enriched Sequential Pattern mining)，该算法在找出模式的同时，也给出了序列模式的时间特征，并且允许用户在挖掘之前对模式的这些时间特征进行限制，提高了序列模式挖掘的灵活性和有用性。     

空间数据挖掘在GIS中的应用研究

近年来，数据挖掘技术的应用越来越广泛。本文研究了空间挖掘技术的理论、过程及方法．并在此基础上提出了一种基于MapX的空间数据挖掘系统结构，以及挖掘过程中所需的数据预处理方法和挖掘算法，为数据挖掘与GIS的集合找到了一种解决方法。     

多媒体开采初探

1 引言对于多媒体的管理,早期采用文件的管理方式。70到80年代期间,数据库学派牵头,采用关键词的描述方法建立媒体数据的索引以达到管理的目的。90年代以后,人们转向研究面向网络环境下的支持基于内容检索的大规模的多媒体数据库。基于内容的检索在一定程度上解决了信息搜索和资源发现的问题,但是基于内容的检索只能获取用户要求的相关信息,而不能从大量多媒体数据中分析出隐含的有价值的模式和知     

多媒体数据的聚簇开采

Internet的普及使多媒体信息的信息量急剧增大,因而,多媒体数据开采逐渐引起人们的关注。文章基于多媒体数据的特点,结合多媒体信息检索技术和数据开采方法,提出了多媒体数据开采系统的基本框架,并给出多媒体数据上的一种聚簇开采算法MDC。     

图像数据挖掘之研究

从介绍一个图像数据挖掘可以采用的系统原型出发,对图像数据挖掘的常用方法和基本过程进行了详细的阐述。     

基于多媒体数据库的关联规则挖掘系统模型

提出了一种多媒体数据库的关联规则挖掘系统模型。介绍了模型的组成，分述了特征提取组件、特征库、挖掘部件3个主要部分，并且介绍了主要的挖掘算法。     

Extracting semantic concepts from images: a decisive feature pattern mining approach

One major challenge in the content-based image retrieval (CBIR) and computer vision research is to bridge the so-called “semantic gap” between low-level visual features and high-level semantic concepts, that is, extracting semantic concepts from a large database of images effectively. In this paper, we tackle the problem by mining the decisive feature patterns (DFPs). Intuitively, a decisive feature pattern is a combination of low-level feature values that are unique and significant for describing a semantic concept. Interesting algorithms are developed to mine the decisive feature patterns and construct a rule base to automatically recognize semantic concepts in images. A systematic performance study on large image databases containing many semantic concepts shows that our method is more effective than some previously proposed methods. Importantly, our method can be generally applied to any domain of semantic concepts and low-level features. Wei Wang received his Ph.D. degree in Computing Science and Engineering from the State University of New York (SUNY) at Buffalo in 2004, under Dr. Aidong Zhang's supervision. He received the B.Eng. in Electrical Engineering from Xi'an Jiaotong University, China in 1995 and the M.Eng. in Computer Engineering from National University of Singapore in 2000, respectively. He joined Motorola Inc. in 2004, where he is currently a senior research engineer in Multimedia Research Lab, Motorola Applications Research Center. His research interests can be summarized as developing novel techniques for multimedia data analysis applications. He is particularly interested in multimedia information retrieval, multimedia mining and association, multimedia database systems, multimedia processing and pattern recognition. He has published 15 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of IADIS International Conference e-Society 2005 and 2006, and has been a reviewer for some leading academic journals and conferences. In 2005, his research prototype of “seamless content consumption” was awarded the “most innovative research concept of the year” from the Motorola Applications Research Center. Dr. Aidong Zhang received her Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at the State University of New York at Buffalo. Her research interests include bioinformatics, data mining, multimedia systems, content-based image retrieval, and database systems. She has authored over 150 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Dr. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACMMultimedia Systems, the International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER Award and SUNY Chancellor's Research Recognition Award.     

多媒体数据集中的数据挖掘:系统框架和方法

分析了目前信息环境对多媒体挖掘技术提出的需求,提出了一种适合多媒体挖掘的系统框架、一般结构和挖掘过程;描述了各种媒体可用于挖掘的特征,阐述相应的适合多媒体挖掘的基本方法;讨论了多媒体挖掘技术今后发展可能遇到的问题和面临的挑战。     

Pattern discovery and change detection of online music query streams

Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed. First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change (changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody structures over continuous music query streams.

视频挖掘研究进展

视频挖掘技术近年来受到了国内外研究者的逐渐关注,但研究还处于初步阶段,实际的应用系统很少,有关概念、系统结构和技术方法仍需深入研究。在回顾国内外研究动态的基础上,对视频挖掘的研究现状进行了归纳、评述;探讨了视频挖掘的概念,清理了视频挖掘与相关技术的联系与区别;展望了视频挖掘研究中的重点问题和解决思路。