This paper focuses on document clustering by clustering algorithm based on a DEnsityTree (CABDET) to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential c...This paper focuses on document clustering by clustering algorithm based on a DEnsityTree (CABDET) to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise (DBSCAN) ′s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node's radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.展开更多
Many algorithms have been implemented for the problem of document categorization. The majority work in this area was achieved for English text, while a very few approaches have been introduced for the Arabic text. The...Many algorithms have been implemented for the problem of document categorization. The majority work in this area was achieved for English text, while a very few approaches have been introduced for the Arabic text. The nature of Arabic text is different from that of the English text and the preprocessing of the Arabic text is more challenging. This is due to Arabic language is a highly inflectional and derivational language that makes document mining a hard and complex task. In this paper, we present an Automatic Arabic documents classification system based on kNN algorithm. Also, we develop an approach to solve keywords extraction and reduction problems by using Document Frequency (DF) threshold method. The results indicate that the ability of the kNN to deal with Arabic text outperforms the other existing systems. The proposed system reached 0.95 micro-recall scores with 850 Arabic texts in 6 different categories.展开更多
To efficiently retrieve relevant document from the rapid proliferation of large information collections, a novel immune algorithm for document query optimization is proposed. The essential ideal of the immune algorith...To efficiently retrieve relevant document from the rapid proliferation of large information collections, a novel immune algorithm for document query optimization is proposed. The essential ideal of the immune algorithm is that the crossover and mutation of operator are constructed according to its own characteristics of information retrieval. Immune operator is adopted to avoid degeneracy. Relevant documents retrieved are merged to a single document list according to rank formula. Experimental results show that the novel immune algorithm can lead to substantial improvements of relevant document retrieval effectiveness.展开更多
基金Science and Technology Development Project of Tianjin(No. 06FZRJGX02400)National Natural Science Foundation of China (No.60603027)
文摘This paper focuses on document clustering by clustering algorithm based on a DEnsityTree (CABDET) to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise (DBSCAN) ′s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node's radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.
文摘Many algorithms have been implemented for the problem of document categorization. The majority work in this area was achieved for English text, while a very few approaches have been introduced for the Arabic text. The nature of Arabic text is different from that of the English text and the preprocessing of the Arabic text is more challenging. This is due to Arabic language is a highly inflectional and derivational language that makes document mining a hard and complex task. In this paper, we present an Automatic Arabic documents classification system based on kNN algorithm. Also, we develop an approach to solve keywords extraction and reduction problems by using Document Frequency (DF) threshold method. The results indicate that the ability of the kNN to deal with Arabic text outperforms the other existing systems. The proposed system reached 0.95 micro-recall scores with 850 Arabic texts in 6 different categories.
基金TheNationalHigh TechDevelopment 863ProgramofChina (No .2 0 0 3AA1Z2 610 )
文摘To efficiently retrieve relevant document from the rapid proliferation of large information collections, a novel immune algorithm for document query optimization is proposed. The essential ideal of the immune algorithm is that the crossover and mutation of operator are constructed according to its own characteristics of information retrieval. Immune operator is adopted to avoid degeneracy. Relevant documents retrieved are merged to a single document list according to rank formula. Experimental results show that the novel immune algorithm can lead to substantial improvements of relevant document retrieval effectiveness.
