مروری بر پژوهش‌های انجام‌شده در خصوص خوشه‌بندی سری‌های زمانی مالی: رویکرد نگاشت دانش
محورهای موضوعی : مدیریت ریسک
مرضیه نوراحمدی
1
,
فاطمه راستی
2
,
حجت الله صادقی
3
1 - دانشجوی دکتری مهندسی مالی، دانشگاه یزد، ایران
2 - گروه مدیریت مالی.دانشکده اقتصاد،مدیریت و حسابداری.دانشگاه یزد.ایران
3 - استادیار مدیریت مالی، دانشگاه یزد، ایران
کلید واژه: خوشه‌بندی, نگاشت دانش, شبکه‌های مالی, سری‌های زمانی مالی, معیارهای فاصله‌ای,
چکیده مقاله :
میزان اطلاعاتی که ما بازیابی و استفاده می‌کنیم، به سرعت افزایش یافته است. داده کاوی فرایند استخراج داده-های مربوط از حجم زیادی از داده‌ها و روش کشف و پیدا کردن الگوی مناسب از حجم زیادی از مجموعه داده‌ها است. خوشه‌بندی یکی از روش‌های معمول تجزیه‌وتحلیل داده‌های آماری و همچنین یکی از بهترین رویکردهای داده‌کاوی است. این رویکرد به‌عنوان یکی از روش‌های یادگیری بدون نظارت، با به‌کارگیری الگوریتم‌هایی، داده‌های سری‌های زمانی را برحسب معیارهای متفاوتی طبقه‌بندی می‌کند. هدف از پژوهش حاضر بررسی انواع کاربردهای خوشه‌بندی و شبکه‌سازی در حوزه‌های مختلف مالی ازجمله ریسک، معاملات الگوریتمی، بانکداری و دیگر موضوعات پرکاربرد در این حوزه است. در این پژوهش با استفاده از پکیج bibliometrix به‌مرور کلیه پژوهش‌های انجام شده در خصوص خوشه بندی پرداخته می‌شود. ضمن استخراج انواع معیارها و رویکردهای خوشه‌بندی به بررسی کاربردهای آن پرداخته‌شده است. این پژوهش با مروری جامع بر کلیه پژوهش‌های این حوزه می‌تواند به‌عنوان جعبه‌ابزاری در جهت ارائه انواع روش‌های خوشه‌بندی محققان را در ایده پردازی و انتخاب روش مناسب در طبقه‌بندی و تحلیل داده‌های مالی یاری دهد.
The amount of information and data we retrieve and use is growing rapidly. Data mining is the process of extracting relevant data from large volumes of data and the method of discovering and finding the appropriate pattern from large volumes of data sets. Clustering is one of the most common methods of statistical data analysis, and also one of the best data mining approaches. This approach, as a method of unsupervised learning, uses algorithms to classify time series data according to different criteria. The purpose of this study is to investigate the types of applications of clustering and networking in various financial fields, including risk, algorithmic trading, banking and other widely used topics in this field. In this research, using the bibliometrix package in the software, all the researches on clustering is reviewed. While extracting various criteria and clustering approaches, its applications have been studied. This study with a comprehensive review of all research in this field can help researchers as a toolbox to provide a variety of clustering methods in ideation and selection of appropriate methods in classifying and analyzing financial data.
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Durante, Fabrizio, Roberta Pappadà, and Nicola Torelli. 2014. “Clustering of Financial Time Series in Risky Scenarios.” Advances in Data Analysis and Classification 8(4):359–76.
Ezugwu, Absalom E., Amit K. Shukla, Moyinoluwa B. Agbaje, Olaide N. Oyelade, Adán José-García, and Jeffery O. Agushaka. 2020. “Automatic Clustering Algorithms: A Systematic Review and Bibliometric Analysis of Relevant Literature.” Neural Computing and Applications 1–60.
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Fraley, Chris, and Adrian E. Raftery. 1998. “How Many Clusters? Which Clustering Method? Answers via Model-Based Cluster Analysis.” The Computer Journal 41(8):578–88.
Frentzos, Elias, Kostas Gratsias, and Yannis Theodoridis. 2007. “Index-Based Most Similar Trajectory Search.” Pp. 816–25 in 2007 IEEE 23rd International Conference on Data Engineering. IEEE.
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