%0 Conference Paper
%F Oral 
%T Fast and Simple Deterministic Seeding of KMeans for Text Document Clustering
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%A Sherkat, Ehsan
%A Velcin, Julien
%A Milios, Evangelos E.
%< avec comité de lecture
%B 9th Conference and Labs of the Evaluation Forum (CLEF)
%C Avignon, France
%P 76-88
%8 2018-09-10
%D 2018
%R 10.1007/978-3-319-98932-7_7
%Z Computer Science [cs]/Web
%Z Computer Science [cs]/Document and Text Processing
%Z Computer Science [cs]/Information Retrieval [cs.IR]
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Statistics [stat]/Machine Learning [stat.ML]Conference papers
%X KMeans is one of the most popular document clustering algorithms. It is usually initialized by random seeds that can drastically impact the final algorithm performance. There exists many random or order-sensitive methods that try to properly initialize KMeans but their problem is that their result is non-deterministic and unrepeatable. Thus KMeans needs to be initialized several times to get a better result, which is a time-consuming operation. In this paper, we introduce a novel deter-AQ1 ministic seeding method for KMeans that is specifically designed for text document clustering. Due to its simplicity, it is fast and can be scaled to large datasets. Experimental results on several real-world datasets demonstrate that the proposed method has overall better performance compared to several deterministic, random, or order-sensitive methods in terms of clustering quality and runtime.
%G English
%2 https://hal.univ-lyon2.fr/hal-01953432/document
%2 https://hal.univ-lyon2.fr/hal-01953432/file/clef-2018.pdf
%L hal-01953432
%U https://hal.univ-lyon2.fr/hal-01953432
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ ERIC
%~ LYON2
%~ UDL
%~ UNIV-LYON