{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:10:33Z","timestamp":1768813833852,"version":"3.49.0"},"reference-count":19,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2019,7]]},"abstract":"<jats:p>Text classification is an important way to handle and organize textual data. Among existing methods of text classification, semi-supervised clustering is a main-stream technique. In the era of \u2018Big data\u2019, the current semi-supervised clustering approaches for text classification generally do not apply for excessive costs in scalability and computing performance for massive text data. Aiming at this problem, this study proposes a scalable text classification algorithm for large-scale text collections, namely D-TESC by modifying a state-of-the-art semi-supervised clustering approach for text classification in a centralized fashion (TESC). D-TESC can process the textual data in a distributed manner to meet a great scalability. The experimental results indicate that (1) the D-TESC algorithm has a comparable classification quality with TESC, and (2) outperforms TESC by average 7.2 times by using eight CPU threads in terms of scalability.<\/jats:p>","DOI":"10.1142\/s0218126619501275","type":"journal-article","created":{"date-parts":[[2018,8,13]],"date-time":"2018-08-13T09:40:17Z","timestamp":1534153217000},"page":"1950127","source":"Crossref","is-referenced-by-count":6,"title":["Use of Distributed Semi-Supervised Clustering for Text Classification"],"prefix":"10.1142","volume":"28","author":[{"given":"Pei","family":"Li","sequence":"first","affiliation":[{"name":"School of Foreign Language, China University of Geosciences, Wuhan, P. R. 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