In order to effectively manage and retrieve the information comprised in vast amount of text documents, powerful text mining tools and techniques are essential. In this paper we evaluate and compare two state-of-the-art data mining tools for clustering high-dimensional text data, Cluto and Gmeans. Several experiments were conducted on three benchmark datasets, and results are analysed in terms of clustering quality, memory and CPU time consumption. We empirically show that Gmeans offers high scalability by sacrificing clustering quality while Cluto presents better clustering quality at the expense of memory and CPU time.

1. An E mpirical C omparison of F ast and E fficient T ools for M ining T extual D ata Volkan TUNALI (Marmara University) A. Yılmaz ÇAMURCU (Marmara University) T. Tugay BİLGİN (Maltepe University)

14. Memory Consumption

15. CPU Time Consumption

16. Purity

17. Entropy

18. F-Measure

19. Normalized Mutual Information