hierarchical clustering
در نشریات گروه کتابداری و مدیریت اطلاعات-
هدف
هدف پژوهش حاضر ترسیم نقشه ساختار دانشی از وضعیت پژوهش در حوزه مدیریت آموزشی ایران است.
روش شناسی:
این پژوهش کاربردی با استفاده از تحلیل هم رخدادی واژگان انجام شده است. جامعه پژوهش، تمامی مقالات علمی اصیل فارسی نمایه شده در «پایگاه مجلات تخصصی نور»، «بانک اطلاعات نشریات کشور» و «مرکز اطلاعات علمی جهاد دانشگاهی» طی سال های 1377 تا 1399 بود. داده ها با به کارگیری سیاهه وارسی گردآوری شدند. برای تجزیه و تحلیل داده ها از نرم افزار Excel، RavaR Matrix، UciNet و از بسته مکمل آن NetDraw در ترسیم شبکه هم رخدادی واژگان استفاده شد.
یافته هااز نظر هم رخدادی واژگان «مدیریت آموزشی»، «رهبری آموزشی» و «مدیریت مدرسه» بیشترین فراوانی را داشته اند. خوشه بندی سلسله مراتبی منجر به تشکیل 3 خوشه شد. خوشه مدیریت آموزشی بالغ ترین و مرکزی ترین خوشه و خوشه رهبری یادگیری و مدیریت مدرسه به ترتیب به عنوان خوشه های توسعه نیافته و درحال ظهور یا زوال شناخته شدند.
نتیجه گیریبررسی ساختار دانش حوزه مدیریت آموزشی وضعیت پژوهش های این حوزه را مشخص کرد که می تواند نقشه راهی برای پژوهش های آتی باشد. در حوزه مدیریت آموزشی موضوعات پیرامون مدیریت مدرسه به عنوان خوشه ای حاشیه ای نیاز به توجه بیشتر در پژوهش های آتی دارد.
کلید واژگان: ترسیم نقشه، ساختار فکری دانش، خوشه بندی سلسله مراتبی، هم رخدادی واژگان، مدیریت آموزشیPurposeCo-word analysis is a tool to depict the intellectual structure of scientific productions. So the purpose of this study is to map the intellectual structure of research status in the field of educational management in Iran.
MethodologyThis applied research has been done by co-word analysis. The research population includes all original Persian scientific articles indexed in databases, named Normagz, SID, and Magiran. Examples with the four main keywords "educational management", "educational leadership", "school management" and "school leadership" along with their sub-topics and their plural and singular states based on searching for the same phrase and without applying a time limit, during the years 1988 to 2020, between 2nd and 20th of July 2020 were searched. These keywords were selected according to "Eric Online Thesaurus", "Persian subject headings", "Congress library subject headings", "Educational management culture book" and "consensus of three professors of educational management". To collect data, first, 1066 articles were retrieved by an advanced search of selected keywords, and 891 articles remained after removing duplicate and unrelated items. In the next step, all the keywords of the articles that were under each title of the article were placed in the Excel file in one column, and the keywords in the titles of the articles were examined one by one by the researcher and after getting the final opinion from the experts in the field and they were given in the continuation of keyword column. At first, 3688 keywords including their repetitions (2221 words excluding repetitions) were obtained from the titles and keywords of the articles. After correcting the typographical and space errors that were too much due to the entry of relatively inappropriate information in Persian databases, the phase of assimilation of words began. In the next step, while checking the keywords one by one, referring to the accepted and common keywords (including 56 keywords) with the opinion of experts in the subject area of the research. Finally, a list of keywords was obtained in which all the words with semantic connections were replaced with the general keyword. A number of keywords completely unrelated to the subject area were also removed based on the opinion of subject experts in the field of educational management. Finally, the number of 2203 keywords, including their repetition, remained for further analysis. Afterwards, the researcher replaced the selected and referred keywords in the entire research data. Excel, RavaR Matrix, and UciNet software were used for data analysis and NetDraw was used to visualize the co-word network.
Findings"Educational Management", "Educational Leadership" and "School Management" have been the most common themes in terms of co-word. In fact, these words form the main discussions related to this field. Hierarchical clustering resulted in the formation of 3 clusters in that the educational management cluster has a central and important position as a mature and developed cluster. Educational Leadership and School Management were identified as underdeveloped and emerging or declining clusters and compared to the educational management cluster, they have attracted less attention in the studies of the educational management field.
ConclusionThe study of the knowledge structure in the field of educational management identified the research status that can be a roadmap for future research. In the field of educational management, the issues surrounding learning leadership and school management as immature and underdeveloped clusters need more attention in future research. Therefore, if attention to these issues is placed on the agenda of the country's educational system, we will see more prosperity in the field of educational management in the future. Therefore, pushing the scientific community toward these issues and conducting research in this direction will be practical and fruitful.
Keywords: Mapping, The intellectual structure of knowledge, Hierarchical Clustering, Co-word analysis, Educational Management -
International Journal of Information Science and Management, Volume:21 Issue: 1, Winter 2023, PP 37 -72
The research aims to visualize and analyze the co-word network and thematic clusters of the intellectual structure in gas turbine thermal management during 1919-2020. The study is applied research in terms of the purpose, which is conducted with a descriptive approach, scientometrics indicators, techniques of co-word, and social network analysis. Data analysis and visualization of the co-word network were represented by VoS Viewer, SPSS, UCINet, and python Software. The top scientific products in the last century were related to engineering subject area and published by the USA country. Seven main clusters were identified for the index keywords, and 20 main clusters were recognized for the author keywords in Scopus regarding the network structure and thematic clusters based on the co-occurrences. Moreover, 38 clusters were identified based on the hierarchical clusters. The clusters, namely heat flux calculations and radiation effects, thermal performance optimization, and operational considerations, have central and major positions in this field and have more potential to maintain and develop themselves in the future. The future of Research and Development (R&D) activities in the area will be focused on novel cycles, heat map development, and Techno-Economic and Risk Analysis (TERA) by utilizing systematic approaches for the identification of heat sinks and sources, fluid modeling, and environmental considerations. In addition, the emerging contributors in the field will be advanced manufacturing and material considerations.
Keywords: Gas turbine thermal management, intellectual structure, Co-Word Analysis, Thematic clusters, Hierarchical Clustering, Strategic Diagram -
International Journal of Digital Content Management, Volume:2 Issue: 1, Winter and Spring 2021, PP 185 -203Purpose
The research aims to visualize and analyze co-word network, and thematic clusters of the intellectual structure in the field of digital content management during 2010-2020.
MethodThe study is applied research with a descriptive approach which is conducted by techniques of co-word, and social network analysis. Data analysis and visualization of the co-word network were represented by SPSS, UCINet, and Python programming language.
Findings8 main clusters are identified. The cluster multimedia content management & retrieval is the most mature and central thematic cluster. The USA and various sub-categories of Computer Science are located in the top ranks of WOS in the field. Most productions were published in 2020. Generally, the Clusters were labeled in two contexts of health and LAM (Libraries, Archives, Museums, and cultural heritage).
ConclusionContent-based management and retrieval are focused on artificial intelligence, decision-supported, knowledge-based and ontological techniques which are conducted as novel approaches and underlying trends in the field.
Keywords: Digital Content Management, Co-word analysis, Hierarchical Clustering, Thematic clusters, Intellectual Structure, Strategic Diagram -
هدف
هدف از پژوهش، ترسیم و تحلیل شبکه هم رخدادی واژگان، و خوشه های موضوعی در حوزه داده های پیوندی در بازه زمانی 2018-1986 است.
روش شناسی:
پژوهش از نظر هدف، نوعی مطالعه کاربردی است که با روش تحلیل هم رخدادی واژگانی با رویکرد توصیفی انجام شده است. خوشه بندی با استفاده از سه شیوه تعیین شده اند. تحلیل و ترسیم شبکه ها با استفاده از نرم افزارهای«وی.او.اس.ویویر»، «اس.پی.اس.اس.» و «یو.سی.آی.نت.» انجام شد.
یافته ها:
از نظر هم رخدادی واژگان، «داده های پیوندی» و «وب معنایی» بیشترین فراوانی را داشته اند. خوشه بندی هم واژگانی منجر به تشکیل 5 خوشه و خوشه بندی سلسله مراتبی منجر به تشکیل 2 خوشه شد. کشور «آمریکا» و حوزه های مختلف «علوم کامپیوتر» بیشترین فراوانی در دسته بندی موضوعی وب علوم در این حوزه را دارند. عمدتا، مطالعات منتشرشده در دو بافت «سلامت» و «میراث فرهنگی» بودند. خوشه «مفاهیم هسته در داده های پیوندی» بالغ ترین و مرکزی ترین خوشه و خوشه «کاربرد داده های پیوندی در بافت میراث فرهنگی » خوشه توسعه یافته اما مجزا می باشد.
نتیجه گیری:
نتایج حاصل می تواند با پررنگ کردن شکاف های موضوعی و جلوگیری از پژوهش های تکراری، روندهای اساسی، و موضوعات هسته و محبوب را شناسایی کند. سیاست گذاران، محققان، و طراحان فناوری های معنایی با آگاهی از این نتایج، می توانند برنامه ریزی پیش بینی کننده ای به منظور توسعه متوازن موضوعات و افزایش کمی و کیفی تولیدات علمی داشته باشند.
کلید واژگان: داده های پیوندی، ساختار فکری دانش، هم رخدادی واژگان، خوشه های موضوعی، خوشه بندی سلسله مراتبی، نمودار راهبردیPurposeThe research aims to visualize and analyze co-word network and thematic clusters in the field of linked data during 1986-2018.
MethodologyThe study is an applied research in terms of the purpose, which conducted by using co-word analysis as a methodology and descriptive approach. Clusters determined by three methods. VOS Viewer, SPSS, and UCINet were used for data analysis and network visualization.
FindingsThe keywords linked data and semantic web in terms of co-word pairs had the highest frequencies. Co-word clustering generated five clusters, while hierarchical clustering produced two clusters. The USA was the most productive country and the highest share of documents published in various sub-categories of the Computer Sciences. Studies mostly published in health and cultural heritage contexts. The cluster core concepts of the semantic web was the most mature and central cluster, while linked data usage in the context of cultural heritage was a well-developed but isolated cluster.
Conclustion:
The results can identify underlying trends and core themes by highlighting thematic gaps to avoid duplicate studies. Policymakers, researchers, and designers of the semantic technologies can plan predictably to develop themes in balance for the future and increase the quality and quantity of scientific outputs.
Keywords: Linked data, Intellectual Structure, Co-word analysis, thematic clusters, Hierarchical Clustering, strategic diagram
- نتایج بر اساس تاریخ انتشار مرتب شدهاند.
- کلیدواژه مورد نظر شما تنها در فیلد کلیدواژگان مقالات جستجو شدهاست. به منظور حذف نتایج غیر مرتبط، جستجو تنها در مقالات مجلاتی انجام شده که با مجله ماخذ هم موضوع هستند.
- در صورتی که میخواهید جستجو را در همه موضوعات و با شرایط دیگر تکرار کنید به صفحه جستجوی پیشرفته مجلات مراجعه کنید.