Download Advanced Data Mining and Applications: 9th International by Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang (auth.), PDF

By Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang (auth.), Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang (eds.)

The two-volume set LNAI 8346 and 8347 constitutes the completely refereed court cases of the ninth foreign convention on complicated information Mining and functions, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 typical papers and sixty four brief papers awarded in those volumes have been conscientiously reviewed and chosen from 222 submissions. The papers integrated in those volumes hide the subsequent themes: opinion mining, habit mining, facts circulate mining, sequential information mining, net mining, picture mining, textual content mining, social community mining, category, clustering, organization rule mining, trend mining, regression, predication, characteristic extraction, id, privateness upkeep, functions, and computing device learning.

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Additional info for Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II

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The parameter k for k-means is set as the ground truth. Fig. 1 shows the F1 -measure and running time of different algorithms as the number of vertices increases. It can be seen that our methods produce more accurate clustering than the competing algorithms. On the other hand, although being provided with the true number of clusters, the clustering quality produced by k-means is not comparable to the rest of the algorithms. Another observation is that as the number of vertices grows, the clustering quality increases, which makes sense in that one can obtain better knowledge of the underlying distribution given more data.

We remove those categories which contains less 500 documents or is multi-labeled, and select a subset of about 20,000 documents in the remaining 103 categories as the testing set. In this experiment, we test our method, KASP in [1] and spectral clustering method [2] respectively on 30 categories, 60 categories and 90 categories of the remaining 103 categories. The 30 categories has the largest value of average sample number per category, while the 60 categories has the second largest value. 2 Evaluation Metrics We test the three methods: GVQ+Spectral clustering, KASP[1], spectral clustering[2] by computing Clustering Accuracy (CA) and Normalized Mutual Information (NMI) on the labels generated by the three methods and their real labels.

Appl. Soft Comput. 7(2), 577–584 (2007) 18. : Clustering high dimensional data: A graphbased relaxed optimization approach. Information Sciences 178(23), 4501–4511 (2008) 19. : SAIL: Summation-bAsed Incremental Learning for Information-Theoretic Text Clustering (2013) 20. : Towards information-theoretic K-means clustering for image indexing. edu Abstract. Conventional clustering algorithms suffer from poor scalability, especially when the data dimension is very large. It may take even days to cluster large datasets.

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