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خوشه‌بندی متن عمیقِ بهبودیافته با استفاده از منیفولد محلی تعبیه‌شده خودرمزگذار | ||
| مدل سازی در مهندسی | ||
| دوره 23، شماره 80، فروردین 1404، صفحه 307-324 اصل مقاله (796.56 K) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.32399.2565 | ||
| نویسندگان | ||
| فاطمه دانشفر* 1؛ امین گلزاری اسکویی2؛ مریم درستی3؛ محمدجواد آقاجانی1 | ||
| 1دانشکده مهندسی، گروه کامپیوتر، دانشگاه کردستان، سنندج، ایران | ||
| 2دانشکده مهندسی کامپیوتر، دانشگاه تبریز، تبریز، ایران | ||
| 3دانشکده مهندسی کامپیوتر، دانشگاه خوارزمی، تهران، ایران | ||
| تاریخ دریافت: 30 آبان 1402، تاریخ بازنگری: 05 خرداد 1403، تاریخ پذیرش: 16 خرداد 1403 | ||
| چکیده | ||
| خوشه‌بندی متن، روشی برای جداسازی اطلاعات از دادگان متنی است که می‌تواند متن را براساس موضوع و احساس طبقه‌بندی کند و اخیرا مورد توجه بسیاری قرار گرفته است. روش‌های مبتنی برخوشه‌بندی عمیق به دلیل دقت بالا، در میان تکنیک‌های خوشه‌بندی از اهمیت ویژه‌ای برخوردار هستند. این روش‌ها شامل دو جزء اصلی کاهش ابعاد و خوشه‌بندی می‌باشد. بسیاری از روش‌های پیشین عمیق، از خودرمزگذار برای کاهش ابعاد استفاده کرده‌اند. با این‌حال، آنها قادر به کاهش ابعاد بر اساس ساختارهای منیفولد نیستند و نمونه‌هایی که شبیه یکدیگر هستند لزوماً در ابعاد پایین نیز در کنار یکدیگر قرار نمی‌گیرند. در این مقاله، ما یک روش خوشه‌بندی متن عمیق را براساس یک منیفولد محلی در لایه خودرمزگذار (DCTMA) توسعه می‌دهیم که از ماتریس‌های شباهت متعدد برای درنظرگرفتن جهت، اندازه و معنا استفاده می‌کند، به‌طوری که ماتریس شباهت نهایی از میانگین این ماتریس‌ها به‌دست می‌آید. ماتریس به‌دست آمده به لایه بازنمایی پنهان در خودرمزگذار اضافه می‌شود. هدف اصلی DCTMA تولید بازنمایی‌های مشابه برای نمونه‌های متعلق به یک خوشه است. پس از کاهش ابعاد با دقت بالا، خوشه‌ها با استفاده از خوشه‌بندی عمیق انتها به انتها شناسایی می‌شوند. نتایج تجربی نشان می‌دهد که روش پیشنهادی در مقایسه با روش‌های پیشرفته فعلی در مجموعه داده‌های متنی، عملکرد شگفت‌آوری دارد. | ||
| کلیدواژه‌ها | ||
| خوشه‌بندی متن؛ خوشه‌بندی عمیق؛ یادگیری عمیق؛ یادگیری منیفولد؛ خودرمزگذار | ||
| عنوان مقاله [English] | ||
| An Improved Deep Text Clustering via Local Manifold of an Autoencoder Embedding | ||
| نویسندگان [English] | ||
| Fatemeh Daneshfar1؛ Amin Golzari oskouei2؛ Maryam Dorosti3؛ Mohammad Javad Aghajani1 | ||
| 1Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Computer Engineering, University of Tabriz, Tabriz, Iran | ||
| 3Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran | ||
| چکیده [English] | ||
| Text clustering is a method for separating specific information from textual data and can even classify text according to topic and sentiment, which has drawn much interest in recent years. Deep clustering methods are especially important among clustering techniques because of their high accuracy. These methods include two main components: dimensionality reduction and clustering. Many earlier efforts have employed autoencoder for dimension reduction; however, they are unable to lower dimensions based on manifold structures, and samples that are like one another are not necessarily placed next to one another in the low dimensional. In the paper, we develop a Deep Text Clustering method based on a local Manifold in the Autoencoder layer (DCTMA) that employs multiple similarity matrices to obtain manifold information, such that this final similarity matrix is obtained from the average of these matrices. The obtained matrix is added to the bottleneck representation layer in the autoencoder. The DCTMA's main goal is to generate similar representations for samples belonging to the same cluster; after dimensionality reduction is achieved with high accuracy, clusters are detected using an end-to-end deep clustering. Experimental results demonstrate that the suggested method performs surprisingly well in comparison to current state-of-the-art methods in text datasets. | ||
| کلیدواژه‌ها [English] | ||
| Text clustering, Deep clustering, Deep learning, Manifold learning, Autoencoder | ||
| مراجع | ||
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