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  • × theme_ss:"Data Mining"
  1. Tiefschürfen in Datenbanken (2002) 0.05
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    Content
    Enthält die Beiträge: Kruse, R., C. Borgelt: Suche im Datendschungel - Borgelt, C. u. R. Kruse: Unsicheres Wissen nutzen - Wrobel, S.: Lern- und Entdeckungsverfahren - Keim, D.A.: Data Mining mit bloßem Auge
    Date
    31.12.1996 19:29:41
    Source
    Spektrum der Wissenschaft. 2002, H.11, S.80-91
    Type
    a
  2. Keim, D.A.: Data Mining mit bloßem Auge (2002) 0.03
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    Date
    31.12.1996 19:29:41
    Source
    Spektrum der Wissenschaft. 2002, H.11, S.88-91
    Type
    a
  3. Kruse, R.; Borgelt, C.: Suche im Datendschungel (2002) 0.03
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    Date
    31.12.1996 19:29:41
    Source
    Spektrum der Wissenschaft. 2002, H.11, S.80-81
    Type
    a
  4. Wrobel, S.: Lern- und Entdeckungsverfahren (2002) 0.03
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    Date
    31.12.1996 19:29:41
    Source
    Spektrum der Wissenschaft. 2002, H.11, S.85-87
    Type
    a
  5. Kulathuramaiyer, N.; Maurer, H.: Implications of emerging data mining (2009) 0.02
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    Abstract
    Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the 'master miners' allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.
    Source
    Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
    Type
    a
  6. Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002) 0.02
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    Date
    31.12.1996 19:29:41
    Source
    Spektrum der Wissenschaft. 2002, H.11, S.82-84
    Type
    a
  7. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.02
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    Abstract
    Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings. Presents a novel method for association generation, that answers all desiderata. The method is different from all existing algorithms and especially suitable to textual databases with binary attributes. Uses subword trees for quick indexing into the required database statistics. Tests the algorithm on the Reuters-22173 database with satisfactory results
    Date
    5. 4.1996 15:29:15
    Source
    Information systems. 22(1997) nos.5/6, S.333-347
    Type
    a
  8. Raan, A.F.J. van; Noyons, E.C.M.: Discovery of patterns of scientific and technological development and knowledge transfer (2002) 0.02
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    Abstract
    This paper addresses a bibliometric methodology to discover the structure of the scientific 'landscape' in order to gain detailed insight into the development of MD fields, their interaction, and the transfer of knowledge between them. This methodology is appropriate to visualize the position of MD activities in relation to interdisciplinary MD developments, and particularly in relation to socio-economic problems. Furthermore, it allows the identification of the major actors. It even provides the possibility of foresight. We describe a first approach to apply bibliometric mapping as an instrument to investigate characteristics of knowledge transfer. In this paper we discuss the creation of 'maps of science' with help of advanced bibliometric methods. This 'bibliometric cartography' can be seen as a specific type of data-mining, applied to large amounts of scientific publications. As an example we describe the mapping of the field neuroscience, one of the largest and fast growing fields in the life sciences. The number of publications covered by this database is about 80,000 per year, the period covered is 1995-1998. Current research is going an to update the mapping for the years 1999-2002. This paper addresses the main lines of the methodology and its application in the study of knowledge transfer.
    Source
    Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase
    Type
    a
  9. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.02
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    Abstract
    Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation
    Date
    5. 4.1996 15:29:15
    Source
    Information systems. 22(1997) nos.5/6, S.349-385
    Type
    a
  10. Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016) 0.02
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    Abstract
    This study proposes a new method to construct and trace the trajectory of conceptual development of a research field by combining main path analysis, citation analysis, and text-mining techniques. Main path analysis, a method used commonly to trace the most critical path in a citation network, helps describe the developmental trajectory of a research field. This study extends the main path analysis method and applies text-mining techniques in the new method, which reflects the trajectory of conceptual development in an academic research field more accurately than citation frequency, which represents only the articles examined. Articles can be merged based on similarity of concepts, and by merging concepts the history of a research field can be described more precisely. The new method was applied to the "h-index" and "text mining" fields. The precision, recall, and F-measures of the h-index were 0.738, 0.652, and 0.658 and those of text-mining were 0.501, 0.653, and 0.551, respectively. Last, this study not only establishes the conceptual trajectory map of a research field, but also recommends keywords that are more precise than those used currently by researchers. These precise keywords could enable researchers to gather related works more quickly than before.
    Date
    21. 7.2016 19:29:19
    Type
    a
  11. Witten, I.H.; Frank, E.: Data Mining : Praktische Werkzeuge und Techniken für das maschinelle Lernen (2000) 0.02
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    Date
    27. 1.1996 10:29:55
    Footnote
    Rez. in: nfd 52(2001), H.7, S.427-428 (T. Mandl)
  12. Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.: From data mining to knowledge discovery in databases (1996) 0.02
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    Type
    a
  13. Mandl, T.: Text mining und data minig (2013) 0.02
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    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
    Type
    a
  14. Klein, H.: Web Content Mining (2004) 0.01
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    Source
    Wissensorganisation und Edutainment: Wissen im Spannungsfeld von Gesellschaft, Gestaltung und Industrie. Proceedings der 7. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Berlin, 21.-23.3.2001. Hrsg.: C. Lehner, H.P. Ohly u. G. Rahmstorf
    Type
    a
  15. Schmid, J.: Data mining : wie finde ich in Datensammlungen entscheidungsrelevante Muster? (1999) 0.01
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    Source
    ARBIDO. 14(1999) H.5, S.11-13
    Type
    a
  16. Chardonnens, A.; Hengchen, S.: Text mining for cultural heritage institutions : a 5-step method for cultural heritage institutions (2017) 0.01
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    Source
    Everything changes, everything stays the same? - Understanding information spaces : Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin/Germany, 13th - 15th March 2017. Eds.: M. Gäde, V. Trkulja u. V. Petras
    Type
    a
  17. Baumgartner, R.: Methoden und Werkzeuge zur Webdatenextraktion (2006) 0.01
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    Source
    Semantic Web: Wege zur vernetzten Wissensgesellschaft. Hrsg.: T. Pellegrini, u. A. Blumauer
    Type
    a
  18. Schwartz, D.: Graphische Datenanalyse für digitale Bibliotheken : Leistungs- und Funktionsumfang moderner Analyse- und Visualisierungsinstrumente (2006) 0.01
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    Source
    Vom Wandel der Wissensorganisation im Informationszeitalter: Festschrift für Walther Umstätter zum 65. Geburtstag, hrsg. von P. Hauke u. K. Umlauf
    Type
    a
  19. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.01
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    Source
    Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
    Type
    a
  20. Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018) 0.01
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    Abstract
    A challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academic and industry. To address this challenge, semantic analysis of textual data is focused in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyse the social data at two levels i.e. the entity level and the domain level. We have chosen Twitter as a social channel challenge for a purpose of concept proof. Domain knowledge is captured in ontologies which are then used to enrich the semantics of tweets provided with specific semantic conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
    Type
    a

Years

Languages

  • e 128
  • d 34
  • sp 1
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