-
Keim, D.A.: Data Mining mit bloßem Auge (2002)
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- Date
- 31.12.1996 19:29:41
-
Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002)
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- 31.12.1996 19:29:41
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Wrobel, S.: Lern- und Entdeckungsverfahren (2002)
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- 31.12.1996 19:29:41
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Heyer, G.; Läuter, M.; Quasthoff, U.; Wolff, C.: Texttechnologische Anwendungen am Beispiel Text Mining (2000)
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- Abstract
- Die zunehmende Menge von Informationen und deren weltweite Verfügbarkeit auf der Basis moderner Internet Technologie machen es erforderlich, Informationen nach inhaltlichen Kriterien zu strukturieren und zu bewerten sowie nach inhaltlichen Kriterien weiter zu verarbeiten. Vom Standpunkt des Benutzers aus sind dabei folgende Fälle zu unterscheiden: Handelt es sich bei den gesuchten Informationen um strukturierle Daten (z.B. in einer SQL-Datenbank) oder unstrukturierte Daten (z.B. grosse Texte)? Ist bekannt, welche Daten benötigt werden und wie sie zu finden sind? Oder ist vor dein Zugriff auf die Daten noch nicht bekannt welche Ergebnisse erwartet werden?
-
Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012)
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Loonus, Y.: Einsatzbereiche der KI und ihre Relevanz für Information Professionals (2017)
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- Abstract
- Es liegt in der Natur des Menschen, Erfahrungen und Ideen in Wort und Schrift mit anderen teilen zu wollen. So produzieren wir jeden Tag gigantische Mengen an Texten, die in digitaler Form geteilt und abgelegt werden. The Radicati Group schätzt, dass 2017 täglich 269 Milliarden E-Mails versendet und empfangen werden. Hinzu kommen größtenteils unstrukturierte Daten wie Soziale Medien, Presse, Websites und firmeninterne Systeme, beispielsweise in Form von CRM-Software oder PDF-Dokumenten. Der weltweite Bestand an unstrukturierten Daten wächst so rasant, dass es kaum möglich ist, seinen Umfang zu quantifizieren. Der Versuch, eine belastbare Zahl zu recherchieren, führt unweigerlich zu diversen Artikeln, die den Anteil unstrukturierter Texte am gesamten Datenbestand auf 80% schätzen. Auch wenn nicht mehr einwandfrei nachvollziehbar ist, woher diese Zahl stammt, kann bei kritischer Reflexion unseres Tagesablaufs kaum bezweifelt werden, dass diese Daten von großer wirtschaftlicher Relevanz sind.
-
Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016)
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Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002)
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- Date
- 31.12.1996 19:29:41
-
Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008)
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-
Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007)
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- Abstract
- Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.
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Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007)
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-
Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015)
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-
Cardie, C.: Empirical methods in information extraction (1997)
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- Date
- 6. 3.1999 13:50:29
-
Tiefschürfen in Datenbanken (2002)
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- Date
- 31.12.1996 19:29:41
-
Bath, P.A.: Data mining in health and medical information (2003)
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- Date
- 23.10.2005 18:29:03
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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
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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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Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018)
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Wiegmann, S.: Hättest du die Titanic überlebt? : Eine kurze Einführung in das Data Mining mit freier Software (2023)
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- Date
- 28. 1.2022 11:05:29
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Varathan, K.D.; Giachanou, A.; Crestani, F.: Comparative opinion mining : a review (2017)
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