Algorithms for clustering data
Algorithms for clustering data
Stemming methodologies over individual query words for an Arabic information retrieval system
Journal of the American Society for Information Science
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Modern Information Retrieval
Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Using N-grams for Arabic text searching
Journal of the American Society for Information Science and Technology
Arabic Stemming Without A Root Dictionary
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
Towards an error-free Arabic stemming
Proceedings of the 2nd ACM workshop on Improving non english web searching
Examining the effect of improved context sensitive morphology on Arabic information retrieval
Semitic '05 Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
Automatic Arabic document categorization based on the Naïve Bayes algorithm
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
| Hi-index | 0.00 |
Arabic Document Clustering has increasingly become an important task for obtaining good results with the unsupervised learning task. This paper aims to evaluate the impact of the five measures (Cosine similarity, Jaccard coefficient, Pearson correlation, Euclidean distance and Averaged Kullback- Leibler Divergence) for Document Clustering with two types of pre-processing morphology-based The Information Science Research Institute (ISRI) is equivalent to the root-based stemmer and light stemmer; and without stemming without morphology) for an Arabic dataset. Stemming is known as a computational process used to reduce words to their stems. For classification, it is categorised as a recall-enhancing or precision-enhancing component. It is concluded that the method of ISRI for words is proved to be better than without stemming methods which use a five similarities/distance measures for Document Clustering.