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Principles of data mining and knowledge discovery (1998) 0.02

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Editor: Zytkow, J.M.

Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.02
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Abstract

Topic discovery is an important means for marketing, e-Business and social science studies. As well, it can be applied to various purposes, such as identifying a group with certain properties and observing the emergence and diminishment of a certain cyber community. Previous topic discovery work (J.M. Kleinberg, Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, California, p. 668) requires manual judgment of usefulness of outcomes and is thus incapable of handling the explosive growth of the Internet. In this paper, we propose the Automatic Topic Discovery (ATD) method, which combines a method of base set construction, a clustering algorithm and an iterative principal eigenvector computation method to discover the topics relevant to a given query without using manual examination. Given a query, ATD returns with topics associated with the query and top representative pages for each topic. Our experiments show that the ATD method performs better than the traditional eigenvector method in terms of computation time and topic discovery quality.

Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.01

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Date: 2. 4.2000 18:01:22

KDD : techniques and applications (1998) 0.01

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Footnote: A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01

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Date: 22.11.1998 18:57:22

Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01

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Source: Information systems. 22(1997) nos.5/6, S.333-347

Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.01

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Source: Information systems. 22(1997) nos.5/6, S.349-385

Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.00

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Date: 22. 3.2013 19:43:01

Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.00

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Source: Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.00

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Date: 7. 3.2019 16:32:22

Information visualization in data mining and knowledge discovery (2002) 0.00

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Date: 23. 3.2008 19:10:22

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