基于自组织映射的期刊主题研究
摘要
学术期刊是科学交流的重要载体。随着科学的发展与人类知识的积累,学术期刊及其刊载论文的数量一直保持快速增长的趋势。期刊数量的不断增长必然导致期刊内容的交叉重复,同一学科或研究领域内可能包括许多期刊,如何从主题的角度有效地收藏、利用并管理学术期刊受到许多机构与个人的关注。早期人们对期刊数量的关心也逐渐转化为对期刊主题内容的注意。期刊主题研究具有重要的学术意义与实践价值,它可以为图书馆等收藏机构有效采购学术期刊、新进入的研究者选择研究方向、研究者选择与其研究内容相关的期刊进行投稿、学术期刊制定相应的发展策略以及科研政策与资助计划的制定提供有意义的参考。
学术期刊通常涉及大量的主题,这种高维数据的特点使得期刊主题研究开展起来不太容易。鉴于此,本文将采用一种可视化的降维方法,即自组织映射(SOM)人工神经网络方法来研究期刊主题,使高维的期刊主题数据显示在低维的SOM空间中,便于研究者观察期刊主题的特点。
本文共分为七个部分：
1.期刊主题研究的理论基础
本章阐述了期刊主题研究的对象、主要内容、研究方法以及走向与趋势。期刊主题研究的对象主要有两种,即期刊及其主题。其研究内容可归纳为八个方面：①期刊主题标引研究,②期刊主题的聚类研究,③特定类别的主题在期刊中的分布研究,④基于主题的期刊分类与聚类研究,⑤特定期刊的主题构成分析,⑥不同国家与地区的期刊主题比较研究,⑦期刊主题热点分析,⑧期刊主题发展趋势研究。期刊主题研究的方法主要是文献计量学方法、内容分析法与专家调查法。潜在语义分析、多维标度以及人工神经网络方法也可用于期刊主题研究。期刊主题研究的发展趋势可归纳为以下几点。首先,期刊主题研究的方法迫切需要从现有的大量繁琐的统计分析工作中解脱出来,引入能有效处理高维数据的新方法。其次,研究内容需要拓展,包括对期刊主题的聚类以及基于主题的期刊聚类等内容。最后,期刊主题研究的层次需要进一步提升。例如,在期刊主题发展趋势研究方面,需要研究如何测量期刊主题整体随着时间变化的程度,而不是仅限于统计归纳个别主题随时间发展的状况。
2.自组织映射用于期刊主题研究的方法论
本章描述了自组织映射(SOM)的原理,比较了两种主要学习算法的优缺点与适用条件,归纳了SOM的几种显示方式,讨论了三种性能较好的SOM软件工具,设计并详细阐述了SOM用于期刊主题研究的方法。SOM是一种无指导学习的人工神经网络方法,其学习算法主要是序列学习与批学习算法,U-matrix图和成分图是两种常见的SOM输出形式。三维输出较平面输出而言,可以避免“边缘效应”,具有更高的准确性。通过广泛调查与试用,笔者发现SOM Toolbox, Viscovery SOMine和Databionic ESOM Tools是三种性能较好的SOM软件工具,并采用SOM Toolbox作为本文的研究工具。为了利用SOM进行期刊主题研究,笔者定义了四种SOM输入矩阵,在Ultsch于2003年定义的U-matrix基础上进行修改,提出一种新的增强型U-matrix。此外,笔者提出了四种新的SOM输出方式,即综合成分图、属性叠加矩阵、属性方差矩阵以及关键属性投影,并详细阐述了它们的定义、原理以及在本文的期刊主题研究中的应用方法。
3.期刊主题聚类研究
本章旨在利用SOM算法对期刊的主题进行聚类,生成等级式的主题目录,为用户查找相关主题、浏览相关文献或改进搜索术语提供建议。笔者以53种与图书情报领域相关的英文期刊为样本,抽取它们在2007年的主题,构造了主题-期刊输入矩阵,利用SOM算法对该矩阵进行训练,将2330个主题映射到163个SOM非空结点上。通过比较自定义的增强型U-matrix与Ultsch于2003年定义的U-matrix应用于训练结果的显示效果,验证了第2章提出的新的增强型U-matrix的有效性与先进性。根据结点的相邻性,将SOM结点中的主题聚为21个类,例如计算机信息管理、计算机信息系统、教育等,分析了各个主题聚类的大小与分布特点,并评价了聚类的效果。最后,笔者将主题聚类的结果与相关研究者的研究结果进行了比较。
4.期刊主题的热点分析
本章旨在发现期刊的热点主题以及这些热点主题在期刊中的分布状况。笔者将属性叠加矩阵应用于第3章的SOM训练结果,识别这53种期刊在2007年的热点主题。结果发现,虽然这些期刊广泛涉及大量的主题,但热点主题仅占全部主题数量的1.1%,主要集中在图书馆、计算机信息系统、教育、企业信息化等领域。笔者将热点主题的分析结果与国内相关研究者的研究结果进行了比较,发现了国内外图书情报期刊在热点主题上的异同点。接着,笔者选择了三种重要期刊,分析了它们的热点主题。最后,通过分析图书馆、信息技术与管理信息化这三类热点主题对应的综合成分图,揭示这三类热点主题主要分布的期刊。
5.期刊的主题相似性与差异研究
本章旨在利用SOM算法根据期刊的主题对期刊进行聚类,识别使期刊之间产生主要差异的关键主题,并确定各期刊聚类的主题特点。笔者构造了期刊-主题输入矩阵,利用SOM算法对该矩阵进行训练,将53种期刊映射到140个SOM结点上。通过分析自定义的增强型U-matrix图,结合结点的相邻程度,将期刊聚成19个类,并对聚类效果进行了评价。然后,笔者将属性方差矩阵应用于第2章对主题-期刊矩阵的训练结果,识别使期刊之间产生主要差异的关键主题；将期刊SOM输出投影到由图书馆类、信息技术类与管理信息化类这三组主题形成的三维空间中,从而分析各期刊聚类的主题特点。
6.期刊主题发展趋势研究
本章旨在利用SOM算法确定期刊主题整体在一段时间内变化的程度,分析主题的时序活跃性以及活跃主题的变化趋势。笔者以Journal of Information Science(JIS)从1981-2007年的主题数据为样本,构造了年份-主题矩阵,利用SOM算法对该矩阵进行训练,将27个年份映射到26个SOM非空结点上,并以彗星模式显示SOM输出。通过分析连续年份在SOM输出中的位置之间的相邻程度,根据自定义的增强型U-matrix图,将27个年份聚成13个类,揭示了该期刊的主题在这27年间的整体变化规律。接着,通过构造主题-年份矩阵,利用SOM算法对该矩阵进行训练,将990个主题映射到153个SOM结点上,应用属性方差矩阵,识别随时间变化较为明显的活跃主题,结合属性叠加矩阵,识别平稳发展的热点主题。最后,利用综合成分图,分析了信息类、计算机与网络类以及图书馆类这三类活跃主题随时间发展的趋势。
7.研究的不足及展望
本章总结了本研究在数据收集与研究内容上的不足,指出后续将扩大研究的期刊范围与时间跨度,研究更多期刊在更长时间跨度内的主题特点；阐述了属性叠加矩阵与属性方差矩阵用于分析期刊的主题总数、主题侧重程度之间的差别以及这些指标随时间变化的规律的基本原理。此外,比较不同国家与地区的图书情报期刊在主题上的相似性与差异将有助于国内图书情报学研究与期刊发展。
图24,表22
Academic journals are important carriers for scientific communication. With the development of science and the accumulation of human knowledge, the volumes of academic journals and journal articles are rapidly increasing, which consequently leads to the overlapping contents of journals. The same discipline or research field may involve in a lot of journals. The issue of how to effectively collect, utilize and manage academic journals from the aspect of subjects attracts the attention of many organizations and individuals. People's concerns shift from the number of journals to their subject contents. Thus, research on journal subjects has academic significance and practical value. It can assist libraries in purchasing academic journals, novice researchers in making decisions on which research topics or groups they should pursue, researchers in contributing to related journals, academic journals in developing appropriate policies, and research funding agencies in making decisions.
Academic journals usually involve in huge amount of subjects. The characteristics of high-dimensional data cause the difficulty in studying journal subjects. Thus, in this dissertation, a visual dimension-reduction method, namely Self-Organizing Map (SOM) is adopted to study journal subjects, which enables users to observe high-dimensional journal subjects in the low-dimensional SOM space conveniently.
The dissertation is composed of seven chapters as follows.
1. The Theory Foundation of Journal Subject Research
This chapter aims to expatiate on the research objects, main contents, research methods and development trends of journal subject research. There are two research objects for journal subject research, namely journals and their subjects. The contents of journal subject research can be listed in eight aspects. They are 1) The indexing of journal subjects,2) The clustering of journal subjects,3) The distribution of a certain category of subjects in journals,4) The classification and clustering of journals based on their subjects,5) The subject composition of a specific journal,6) The comparison among subjects of journals from different countries or districts,7) The analysis of hot subjects of journals, and 8) The development trends of journal subjects. The research methods of journal subject research mainly include bibliometrics, content analysis and expert surveys. In addition, Latent Semantic Analysis (LSA), Multidimensional Scaling (MDS) and artificial neural network (ANN) techniques can also be employed to study journal subjects. The development trends of journal subject research can be summarized in the following aspects. First, researchers need to extricate journal subject studies from heavy and complex statistical tasks and introduce novel and effective methods which are capable of processing high-dimensional data. Second, the research contents need to be broadened to include the clustering of subjects, the clustering of journals based on their subjects and so on. Finally, the research level needs to be improved. For example, when studying the development trends of journal subjects, in addition to summarize the development status of individual subjects, researchers need to measure how much the journal subjects have changed on the whole as the time passes.
2. The Methodology of Applying SOM to Journal Subject Research
This chapter aims to describe the principle of Self-Organizing Map (SOM), to compare the advantages and disadvantages of two learning algorithms, to summarize several display styles, to discuss three SOM tools of high-performance and to elaborate on how the SOM technique is applied to journal subject research. SOM is an unsupervised artificial neural network technique which mainly has two learning algorithm, namely sequential learning and batch learning. U-matrix and component plane are two common kinds of SOM display styles. Compared with plane output, three-dimensional output can avoid "border effect" and is more accurate. A comprehensive survey and some trials reveal that three SOM tools have high performance. They are SOM Toolbox, Viscovery SOMine and Databionic ESOM Tools. In this study, SOM Toolbox is utilized. To study journal subjects with the SOM technique, four SOM input matrices are constructed, a novel enhanced U-matrix is defined based on the U-matrix defined by Ultsch in 2003. The author presents four new SOM display styles and explains how they are employed to study journal subjects. They are named Integrative Component Plane, Attribute Accumulative Matrix, Attribute Variance Matrix and Key Attribute Projection.
3. The Clustering Analysis of Journal Subjects
This chapter aims to cluster journal subjects with the SOM technique and generate a hierarchical subject directory to provide suggestions for users to locate relevant subjects, to browse relevant literature and to modify search terms. Fifty-three English journals in the field of library and information science are selected as samples, from which subjects reported in 2007 are extracted. A Subject-Journal input matrix is constructed and trained with the SOM technique so that 2330 subjects are projected onto 163 non-empty SOM nodes. A comparison between the self-defined enhanced U-matrix and the U-matrix presented by Ultsch in 2003 verifies the effectiveness and advantage of the self-defined enhanced U-matrix. The subjects are clustered in 21 categories based on the vicinity of SOM nodes, for example, computer information management, computer information system, education, etc. The size and distribution characteristics of subject clusters are analyzed and the clustering effect is evaluated. The clustering results are also compared with relevant research findings.
4. Analysis of Hot Subjects of Journals
This chapter aims to discover the hot subjects and their distribution among journals. The Attribute Accumulative Matrix is applied to the SOM display in Chapter 3 to identify the hot subjects among the 53 journals in 2007. The results show that although a lot of subjects were involved in these journals, the number of hot subjects only occupied 1.1% of the total subjects and focused on the field of library, computer information system, education and enterprise information. The identified hot subjects are compared with domestic relevant research and the differences between hot subjects with Chinese LIS journals and those with English LIS journals are revealed. Then three important journals are selected and their hot subjects are analyzed. Finally, three groups of hot subjects, namely library, information technology and management information, are selected, upon which corresponding Integrative Component Planes are analyzed to discover the important journals in which the three groups of hot subjects are mainly distributed.
5. Similarities and Differences of Journals in Terms of Subjects
This chapter aims to cluster journals with the SOM technique based on their subjects, to identify the key subjects that differentiate individual journals and to determine the subject characteristics of journal clusters. The Journal-Subject input matrix is constructed and trained with the SOM technique. Fifty-three journals are projected onto 140 SOM nodes and clustered into 19 categories based on self-defined enhanced U-matrix and the vicinities of SOM nodes. The clustering effect is evaluated. Then the Attribute Variance Matrix is applied to the SOM display obtained from Subject-Journal matrix in Chapter 2 to identify the key subjects that contribute the most to the differences among individual journals. The journal SOM display is projected onto the three-dimensional space formed by library, information technology and management information-related subjects to analyze the subject characteristics of journal clusters.
6. Development Trends of Journal Subj ects
This chapter aims to employ the SOM technique to determine how much the journal subjects have changed in a certain period on the whole, to analyze the activeness of subjects in a certain period and the development trends of active subjects. Journal of Information Science (JIS) is selected as the sample and it subjects from 1981 through 2007 are collected. A Year-Subject input matrix is constructed and trained with the SOM technique. The 27 years are projected onto 26 non-empty SOM nodes and the learning results are displayed with the comet mode. The vicinity of the SOM nodes onto which consecutive years are projected is analyzed and the 27 years are clustered into 13 categories based on self-defined enhanced U-matrix. The development course of the subjects of JIS is revealed. Then a Subject-Year input matrix is constructed and trained with the SOM technique. Nine hundred and ninety subjects are projected onto 153 SOM nodes. The Attribute Variance Matrix is applied to identify the active subjects that changed much as time passed. With the help of Attribute Accumulative Matrix, the hot subjects that developed smoothly are identified. Finally, Integrative Component Plane is applied to analyze the development trends of three kinds of active subjects, namely information, computer & network, and library.
7. The Limitation and Future Research Direction
This chapter aims to point out the limitation of this study in terms of data collection and research contents. More journals and longer period will be involved in future research. Attribute Accumulative Matrix and Attribute Variance Matrix can be employed to analyze the total number of subjects, the differences among subject focus and how these indexes change as time passes. Moreover, the comparison among subjects of journals from different countries or districts will help domestic research and journals develop in the field of library and information science.
Figures:24, Tables:22
学术期刊通常涉及大量的主题,这种高维数据的特点使得期刊主题研究开展起来不太容易。鉴于此,本文将采用一种可视化的降维方法,即自组织映射(SOM)人工神经网络方法来研究期刊主题,使高维的期刊主题数据显示在低维的SOM空间中,便于研究者观察期刊主题的特点。
本文共分为七个部分：
1.期刊主题研究的理论基础
本章阐述了期刊主题研究的对象、主要内容、研究方法以及走向与趋势。期刊主题研究的对象主要有两种,即期刊及其主题。其研究内容可归纳为八个方面：①期刊主题标引研究,②期刊主题的聚类研究,③特定类别的主题在期刊中的分布研究,④基于主题的期刊分类与聚类研究,⑤特定期刊的主题构成分析,⑥不同国家与地区的期刊主题比较研究,⑦期刊主题热点分析,⑧期刊主题发展趋势研究。期刊主题研究的方法主要是文献计量学方法、内容分析法与专家调查法。潜在语义分析、多维标度以及人工神经网络方法也可用于期刊主题研究。期刊主题研究的发展趋势可归纳为以下几点。首先,期刊主题研究的方法迫切需要从现有的大量繁琐的统计分析工作中解脱出来,引入能有效处理高维数据的新方法。其次,研究内容需要拓展,包括对期刊主题的聚类以及基于主题的期刊聚类等内容。最后,期刊主题研究的层次需要进一步提升。例如,在期刊主题发展趋势研究方面,需要研究如何测量期刊主题整体随着时间变化的程度,而不是仅限于统计归纳个别主题随时间发展的状况。
2.自组织映射用于期刊主题研究的方法论
本章描述了自组织映射(SOM)的原理,比较了两种主要学习算法的优缺点与适用条件,归纳了SOM的几种显示方式,讨论了三种性能较好的SOM软件工具,设计并详细阐述了SOM用于期刊主题研究的方法。SOM是一种无指导学习的人工神经网络方法,其学习算法主要是序列学习与批学习算法,U-matrix图和成分图是两种常见的SOM输出形式。三维输出较平面输出而言,可以避免“边缘效应”,具有更高的准确性。通过广泛调查与试用,笔者发现SOM Toolbox, Viscovery SOMine和Databionic ESOM Tools是三种性能较好的SOM软件工具,并采用SOM Toolbox作为本文的研究工具。为了利用SOM进行期刊主题研究,笔者定义了四种SOM输入矩阵,在Ultsch于2003年定义的U-matrix基础上进行修改,提出一种新的增强型U-matrix。此外,笔者提出了四种新的SOM输出方式,即综合成分图、属性叠加矩阵、属性方差矩阵以及关键属性投影,并详细阐述了它们的定义、原理以及在本文的期刊主题研究中的应用方法。
3.期刊主题聚类研究
本章旨在利用SOM算法对期刊的主题进行聚类,生成等级式的主题目录,为用户查找相关主题、浏览相关文献或改进搜索术语提供建议。笔者以53种与图书情报领域相关的英文期刊为样本,抽取它们在2007年的主题,构造了主题-期刊输入矩阵,利用SOM算法对该矩阵进行训练,将2330个主题映射到163个SOM非空结点上。通过比较自定义的增强型U-matrix与Ultsch于2003年定义的U-matrix应用于训练结果的显示效果,验证了第2章提出的新的增强型U-matrix的有效性与先进性。根据结点的相邻性,将SOM结点中的主题聚为21个类,例如计算机信息管理、计算机信息系统、教育等,分析了各个主题聚类的大小与分布特点,并评价了聚类的效果。最后,笔者将主题聚类的结果与相关研究者的研究结果进行了比较。
4.期刊主题的热点分析
本章旨在发现期刊的热点主题以及这些热点主题在期刊中的分布状况。笔者将属性叠加矩阵应用于第3章的SOM训练结果,识别这53种期刊在2007年的热点主题。结果发现,虽然这些期刊广泛涉及大量的主题,但热点主题仅占全部主题数量的1.1%,主要集中在图书馆、计算机信息系统、教育、企业信息化等领域。笔者将热点主题的分析结果与国内相关研究者的研究结果进行了比较,发现了国内外图书情报期刊在热点主题上的异同点。接着,笔者选择了三种重要期刊,分析了它们的热点主题。最后,通过分析图书馆、信息技术与管理信息化这三类热点主题对应的综合成分图,揭示这三类热点主题主要分布的期刊。
5.期刊的主题相似性与差异研究
本章旨在利用SOM算法根据期刊的主题对期刊进行聚类,识别使期刊之间产生主要差异的关键主题,并确定各期刊聚类的主题特点。笔者构造了期刊-主题输入矩阵,利用SOM算法对该矩阵进行训练,将53种期刊映射到140个SOM结点上。通过分析自定义的增强型U-matrix图,结合结点的相邻程度,将期刊聚成19个类,并对聚类效果进行了评价。然后,笔者将属性方差矩阵应用于第2章对主题-期刊矩阵的训练结果,识别使期刊之间产生主要差异的关键主题；将期刊SOM输出投影到由图书馆类、信息技术类与管理信息化类这三组主题形成的三维空间中,从而分析各期刊聚类的主题特点。
6.期刊主题发展趋势研究
本章旨在利用SOM算法确定期刊主题整体在一段时间内变化的程度,分析主题的时序活跃性以及活跃主题的变化趋势。笔者以Journal of Information Science(JIS)从1981-2007年的主题数据为样本,构造了年份-主题矩阵,利用SOM算法对该矩阵进行训练,将27个年份映射到26个SOM非空结点上,并以彗星模式显示SOM输出。通过分析连续年份在SOM输出中的位置之间的相邻程度,根据自定义的增强型U-matrix图,将27个年份聚成13个类,揭示了该期刊的主题在这27年间的整体变化规律。接着,通过构造主题-年份矩阵,利用SOM算法对该矩阵进行训练,将990个主题映射到153个SOM结点上,应用属性方差矩阵,识别随时间变化较为明显的活跃主题,结合属性叠加矩阵,识别平稳发展的热点主题。最后,利用综合成分图,分析了信息类、计算机与网络类以及图书馆类这三类活跃主题随时间发展的趋势。
7.研究的不足及展望
本章总结了本研究在数据收集与研究内容上的不足,指出后续将扩大研究的期刊范围与时间跨度,研究更多期刊在更长时间跨度内的主题特点；阐述了属性叠加矩阵与属性方差矩阵用于分析期刊的主题总数、主题侧重程度之间的差别以及这些指标随时间变化的规律的基本原理。此外,比较不同国家与地区的图书情报期刊在主题上的相似性与差异将有助于国内图书情报学研究与期刊发展。
图24,表22
Academic journals are important carriers for scientific communication. With the development of science and the accumulation of human knowledge, the volumes of academic journals and journal articles are rapidly increasing, which consequently leads to the overlapping contents of journals. The same discipline or research field may involve in a lot of journals. The issue of how to effectively collect, utilize and manage academic journals from the aspect of subjects attracts the attention of many organizations and individuals. People's concerns shift from the number of journals to their subject contents. Thus, research on journal subjects has academic significance and practical value. It can assist libraries in purchasing academic journals, novice researchers in making decisions on which research topics or groups they should pursue, researchers in contributing to related journals, academic journals in developing appropriate policies, and research funding agencies in making decisions.
Academic journals usually involve in huge amount of subjects. The characteristics of high-dimensional data cause the difficulty in studying journal subjects. Thus, in this dissertation, a visual dimension-reduction method, namely Self-Organizing Map (SOM) is adopted to study journal subjects, which enables users to observe high-dimensional journal subjects in the low-dimensional SOM space conveniently.
The dissertation is composed of seven chapters as follows.
1. The Theory Foundation of Journal Subject Research
This chapter aims to expatiate on the research objects, main contents, research methods and development trends of journal subject research. There are two research objects for journal subject research, namely journals and their subjects. The contents of journal subject research can be listed in eight aspects. They are 1) The indexing of journal subjects,2) The clustering of journal subjects,3) The distribution of a certain category of subjects in journals,4) The classification and clustering of journals based on their subjects,5) The subject composition of a specific journal,6) The comparison among subjects of journals from different countries or districts,7) The analysis of hot subjects of journals, and 8) The development trends of journal subjects. The research methods of journal subject research mainly include bibliometrics, content analysis and expert surveys. In addition, Latent Semantic Analysis (LSA), Multidimensional Scaling (MDS) and artificial neural network (ANN) techniques can also be employed to study journal subjects. The development trends of journal subject research can be summarized in the following aspects. First, researchers need to extricate journal subject studies from heavy and complex statistical tasks and introduce novel and effective methods which are capable of processing high-dimensional data. Second, the research contents need to be broadened to include the clustering of subjects, the clustering of journals based on their subjects and so on. Finally, the research level needs to be improved. For example, when studying the development trends of journal subjects, in addition to summarize the development status of individual subjects, researchers need to measure how much the journal subjects have changed on the whole as the time passes.
2. The Methodology of Applying SOM to Journal Subject Research
This chapter aims to describe the principle of Self-Organizing Map (SOM), to compare the advantages and disadvantages of two learning algorithms, to summarize several display styles, to discuss three SOM tools of high-performance and to elaborate on how the SOM technique is applied to journal subject research. SOM is an unsupervised artificial neural network technique which mainly has two learning algorithm, namely sequential learning and batch learning. U-matrix and component plane are two common kinds of SOM display styles. Compared with plane output, three-dimensional output can avoid "border effect" and is more accurate. A comprehensive survey and some trials reveal that three SOM tools have high performance. They are SOM Toolbox, Viscovery SOMine and Databionic ESOM Tools. In this study, SOM Toolbox is utilized. To study journal subjects with the SOM technique, four SOM input matrices are constructed, a novel enhanced U-matrix is defined based on the U-matrix defined by Ultsch in 2003. The author presents four new SOM display styles and explains how they are employed to study journal subjects. They are named Integrative Component Plane, Attribute Accumulative Matrix, Attribute Variance Matrix and Key Attribute Projection.
3. The Clustering Analysis of Journal Subjects
This chapter aims to cluster journal subjects with the SOM technique and generate a hierarchical subject directory to provide suggestions for users to locate relevant subjects, to browse relevant literature and to modify search terms. Fifty-three English journals in the field of library and information science are selected as samples, from which subjects reported in 2007 are extracted. A Subject-Journal input matrix is constructed and trained with the SOM technique so that 2330 subjects are projected onto 163 non-empty SOM nodes. A comparison between the self-defined enhanced U-matrix and the U-matrix presented by Ultsch in 2003 verifies the effectiveness and advantage of the self-defined enhanced U-matrix. The subjects are clustered in 21 categories based on the vicinity of SOM nodes, for example, computer information management, computer information system, education, etc. The size and distribution characteristics of subject clusters are analyzed and the clustering effect is evaluated. The clustering results are also compared with relevant research findings.
4. Analysis of Hot Subjects of Journals
This chapter aims to discover the hot subjects and their distribution among journals. The Attribute Accumulative Matrix is applied to the SOM display in Chapter 3 to identify the hot subjects among the 53 journals in 2007. The results show that although a lot of subjects were involved in these journals, the number of hot subjects only occupied 1.1% of the total subjects and focused on the field of library, computer information system, education and enterprise information. The identified hot subjects are compared with domestic relevant research and the differences between hot subjects with Chinese LIS journals and those with English LIS journals are revealed. Then three important journals are selected and their hot subjects are analyzed. Finally, three groups of hot subjects, namely library, information technology and management information, are selected, upon which corresponding Integrative Component Planes are analyzed to discover the important journals in which the three groups of hot subjects are mainly distributed.
5. Similarities and Differences of Journals in Terms of Subjects
This chapter aims to cluster journals with the SOM technique based on their subjects, to identify the key subjects that differentiate individual journals and to determine the subject characteristics of journal clusters. The Journal-Subject input matrix is constructed and trained with the SOM technique. Fifty-three journals are projected onto 140 SOM nodes and clustered into 19 categories based on self-defined enhanced U-matrix and the vicinities of SOM nodes. The clustering effect is evaluated. Then the Attribute Variance Matrix is applied to the SOM display obtained from Subject-Journal matrix in Chapter 2 to identify the key subjects that contribute the most to the differences among individual journals. The journal SOM display is projected onto the three-dimensional space formed by library, information technology and management information-related subjects to analyze the subject characteristics of journal clusters.
6. Development Trends of Journal Subj ects
This chapter aims to employ the SOM technique to determine how much the journal subjects have changed in a certain period on the whole, to analyze the activeness of subjects in a certain period and the development trends of active subjects. Journal of Information Science (JIS) is selected as the sample and it subjects from 1981 through 2007 are collected. A Year-Subject input matrix is constructed and trained with the SOM technique. The 27 years are projected onto 26 non-empty SOM nodes and the learning results are displayed with the comet mode. The vicinity of the SOM nodes onto which consecutive years are projected is analyzed and the 27 years are clustered into 13 categories based on self-defined enhanced U-matrix. The development course of the subjects of JIS is revealed. Then a Subject-Year input matrix is constructed and trained with the SOM technique. Nine hundred and ninety subjects are projected onto 153 SOM nodes. The Attribute Variance Matrix is applied to identify the active subjects that changed much as time passed. With the help of Attribute Accumulative Matrix, the hot subjects that developed smoothly are identified. Finally, Integrative Component Plane is applied to analyze the development trends of three kinds of active subjects, namely information, computer & network, and library.
7. The Limitation and Future Research Direction
This chapter aims to point out the limitation of this study in terms of data collection and research contents. More journals and longer period will be involved in future research. Attribute Accumulative Matrix and Attribute Variance Matrix can be employed to analyze the total number of subjects, the differences among subject focus and how these indexes change as time passes. Moreover, the comparison among subjects of journals from different countries or districts will help domestic research and journals develop in the field of library and information science.
Figures:24, Tables:22
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3 Freeman, R.T., and Yin, H. Tree view self-organisation of web content. Neurocomputing,2005,63,415-446.
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4 Wu, Y., Takatsuka, M. The Geodesic Self-Organizing Map and its error analysis. Proceedings of the Twenty-eighth Australasian conference on Computer Science, Vol.38,2005,343-351.
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