Khazaei, AtefehAtefehKhazaeiGhasemzadeh, MohammadMohammadGhasemzadehGollmann, DieterDieterGollmann2021-11-192021-11-192018-027th International Conference on Software and Computer Applications, ICSCA 2018: 115-118978-1-4503-5414-1http://hdl.handle.net/11420/11011Texts have inherent overlapping, therefore for clustering textual data, the overlapping clustering algorithms are more appropriate. In this regard, a major challenge is that they are very slow in clustering big volumes of textual data. Among others, OKM and OSOM are two important overlapping clustering algorithms. In this study, we have implemented and compared the performance of these two algorithms. The experimental results of our study show that OKM clusters have better overlap sizes when these algorithms are used for clustering textual data. Since both of them require much time to complete, none of these two algorithms is suitable for clustering textual data. Therefore we mastermind a fast overlapping version of SOM which is suitable for this purpose.enOKMOSOMOverlapping clustering algorithmTextual dataComputer Science, Information and General Works::004: Computer SciencesConference Paper10.1145/3185089.3185113Conference Paper