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Handbuch Web Mining im Marketing : Konzepte, Systeme, Fallstudien (2002)
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- X, 509 S
-
Decker, B.: Data Mining in Öffentlichen Bibliotheken (2000)
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Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005)
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Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
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- 2. 4.2000 18:01:22
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Perugini, S.; Ramakrishnan, N.: Mining Web functional dependencies for flexible information access (2007)
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- Abstract
- We present an approach to enhancing information access through Web structure mining in contrast to traditional approaches involving usage mining. Specifically, we mine the hardwired hierarchical hyperlink structure of Web sites to identify patterns of term-term co-occurrences we call Web functional dependencies (FDs). Intuitively, a Web FD x -> y declares that all paths through a site involving a hyperlink labeled x also contain a hyperlink labeled y. The complete set of FDs satisfied by a site help characterize (flexible and expressive) interaction paradigms supported by a site, where a paradigm is the set of explorable sequences therein. We describe algorithms for mining FDs and results from mining several hierarchical Web sites and present several interface designs that can exploit such FDs to provide compelling user experiences.
-
KDD : techniques and applications (1998)
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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
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Analytische Informationssysteme : Data Warehouse, On-Line Analytical Processing, Data Mining (1998)
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- X,491 S
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Wong, S.K.M.; Butz, C.J.; Xiang, X.: Automated database schema design using mined data dependencies (1998)
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Wu, X.: Rule induction with extension matrices (1998)
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Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
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Sun, X.; Lin, H.: Topical community detection from mining user tagging behavior and interest (2013)
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Song, J.; Huang, Y.; Qi, X.; Li, Y.; Li, F.; Fu, K.; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents (2016)
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Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
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- Date
- 22.11.1998 18:57:22
-
Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999)
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- Date
- 17. 7.2002 19:22:06
-
Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997)
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- Source
- Information systems. 22(1997) nos.5/6, S.333-347
-
Shi, X.; Yang, C.C.: Mining related queries from Web search engine query logs using an improved association rule mining model (2007)
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Liu, Y.; Huang, X.; An, A.: Personalized recommendation with adaptive mixture of markov models (2007)
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Witschel, H.F.: Text, Wörter, Morpheme : Möglichkeiten einer automatischen Terminologie-Extraktion (2004)
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Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015)
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Varathan, K.D.; Giachanou, A.; Crestani, F.: Comparative opinion mining : a review (2017)
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- Abstract
- Opinion mining refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyze public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining which deals with identifying and extracting information that is expressed in a comparative form (e.g., "paper X is better than the Y"). Comparative opinion mining plays a very important role when one tries to evaluate something because it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from the perspective of techniques and the other from the perspective of comparative opinion elements. It also incorporates preprocessing tools as well as data set that were used by past researchers that can be useful to future researchers in the field of comparative opinion mining.
