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  • × theme_ss:"Data Mining"
  1. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.05 
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    Date
    17. 7.2002 19:22:06
    Theme
    Information Resources Management
  2. Medien-Informationsmanagement : Archivarische, dokumentarische, betriebswirtschaftliche, rechtliche und Berufsbild-Aspekte ; [Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar und Folgetagung 2001 in Köln] (2003) 0.05 
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    Abstract
    Als in den siebziger Jahren des vergangenen Jahrhunderts immer häufiger die Bezeichnung Informationsmanager für Leute propagiert wurde, die bis dahin als Dokumentare firmierten, wurde dies in den etablierten Kreisen der Archivare und Bibliothekare gelegentlich belächelt und als Zeichen einer Identitätskrise oder jedenfalls einer Verunsicherung des damit überschriebenen Berufsbilds gewertet. Für den Berufsstand der Medienarchivare/Mediendokumentare, die sich seit 1960 in der Fachgruppe 7 des Vereins, später Verbands deutscher Archivare (VdA) organisieren, gehörte diese Verortung im Zeichen neuer inhaltlicher Herausforderungen (Informationsflut) und Technologien (EDV) allerdings schon früh zu den Selbstverständlichkeiten des Berufsalltags. "Halt, ohne uns geht es nicht!" lautete die Überschrift eines Artikels im Verbandsorgan "Info 7", der sich mit der Einrichtung von immer mächtigeren Leitungsnetzen und immer schnelleren Datenautobahnen beschäftigte. Information, Informationsgesellschaft: diese Begriffe wurden damals fast nur im technischen Sinne verstanden. Die informatisierte, nicht die informierte Gesellschaft stand im Vordergrund - was wiederum Kritiker auf den Plan rief, von Joseph Weizenbaum in den USA bis hin zu den Informations-Ökologen in Bremen. Bei den nationalen, manchmal auch nur regionalen Projekten und Modellversuchen mit Datenautobahnen - auch beim frühen Btx - war nie so recht deutlich geworden, welche Inhalte in welcher Gestalt durch diese Netze und Straßen gejagt werden sollten und wer diese Inhalte eigentlich selektieren, portionieren, positionieren, kurz: managen sollte. Spätestens mit dem World Wide Web sind diese Projekte denn auch obsolet geworden, jedenfalls was die Hardware und Software anging. Geblieben ist das Thema Inhalte (neudeutsch: Content). Und - immer drängender im nicht nur technischen Verständnis - das Thema Informationsmanagement. MedienInformationsManagement war die Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar überschrieben, und auch die Folgetagung 2001 in Köln, die der multimedialen Produktion einen dokumentarischen Pragmatismus gegenüber stellte, handelte vom Geschäftsfeld Content und von Content-Management-Systemen. Die in diesem 6. Band der Reihe Beiträge zur Mediendokumentation versammelten Vorträge und Diskussionsbeiträge auf diesen beiden Tagungen beleuchten das Titel-Thema aus den verschiedensten Blickwinkeln: archivarischen, dokumentarischen, kaufmännischen, berufsständischen und juristischen. Deutlich wird dabei, daß die Berufsbezeichnung Medienarchivarln/Mediendokumentarln ziemlich genau für all das steht, was heute mit sog. alten wie neuen Medien im organisatorischen, d.h. ordnenden und vermittelnden Sinne geschieht. Im besonderen Maße trifft dies auf das Internet und die aus ihm geborenen Intranets zu. Beide bedürfen genauso der ordnenden Hand, die sich an den alten Medien, an Buch, Zeitung, Tonträger, Film etc. geschult hat, denn sie leben zu großen Teilen davon. Daß das Internet gleichwohl ein Medium sui generis ist und die alten Informationsberufe vor ganz neue Herausforderungen stellt - auch das durchzieht die Beiträge von Weimar und Köln.
    Content
    Enthält u.a. die Beiträge (Dokumentarische Aspekte): Günter Perers/Volker Gaese: Das DocCat-System in der Textdokumentation von Gr+J (Weimar 2000) Thomas Gerick: Finden statt suchen. Knowledge Retrieval in Wissensbanken. Mit organisiertem Wissen zu mehr Erfolg (Weimar 2000) Winfried Gödert: Aufbereitung und Rezeption von Information (Weimar 2000) Elisabeth Damen: Klassifikation als Ordnungssystem im elektronischen Pressearchiv (Köln 2001) Clemens Schlenkrich: Aspekte neuer Regelwerksarbeit - Multimediales Datenmodell für ARD und ZDF (Köln 2001) Josef Wandeler: Comprenez-vous only Bahnhof'? - Mehrsprachigkeit in der Mediendokumentation (Köln 200 1)
    Date
    11. 5.2008 19:49:22
    LCSH
    Information technology / Management / Congresses
    Subject
    Information technology / Management / Congresses
  3. Liu, W.; Weichselbraun, A.; Scharl, A.; Chang, E.: Semi-automatic ontology extension using spreading activation (2005) 0.04 
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    Abstract
    This paper describes a system to semi-automatically extend and refine ontologies by mining textual data from the Web sites of international online media. Expanding a seed ontology creates a semantic network through co-occurrence analysis, trigger phrase analysis, and disambiguation based on the WordNet lexical dictionary. Spreading activation then processes this semantic network to find the most probable candidates for inclusion in an extended ontology. Approaches to identifying hierarchical relationships such as subsumption, head noun analysis and WordNet consultation are used to confirm and classify the found relationships. Using a seed ontology on "climate change" as an example, this paper demonstrates how spreading activation improves the result by naturally integrating the mentioned methods.
    Source
    Journal of universal knowledge management. 0(2005) no.1, S.50-58
  4. Pons-Porrata, A.; Berlanga-Llavori, R.; Ruiz-Shulcloper, J.: Topic discovery based on text mining techniques (2007) 0.04 
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    Abstract
    In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.
    Source
    Information processing and management. 43(2007) no.3, S.752-768
  5. Berendt, B.; Krause, B.; Kolbe-Nusser, S.: Intelligent scientific authoring tools : interactive data mining for constructive uses of citation networks (2010) 0.04 
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    Abstract
    Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.
    Source
    Information processing and management. 46(2010) no.1, S.1-10
  6. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.04 
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    Abstract
    Das Buch richtet sich an Praktiker in Unternehmen, die sich mit der Analyse von großen Datenbeständen beschäftigen. Nach einem kurzen Theorieteil werden vier Fallstudien aus dem Customer Relationship Management eines Versandhändlers bearbeitet. Dabei wurden acht führende Softwarelösungen verwendet: der Intelligent Miner von IBM, der Enterprise Miner von SAS, Clementine von SPSS, Knowledge Studio von Angoss, der Delta Miner von Bissantz, der Business Miner von Business Object und die Data Engine von MIT. Im Rahmen der Fallstudien werden die Stärken und Schwächen der einzelnen Lösungen deutlich, und die methodisch-korrekte Vorgehensweise beim Data Mining wird aufgezeigt. Beides liefert wertvolle Entscheidungshilfen für die Auswahl von Standardsoftware zum Data Mining und für die praktische Datenanalyse.
    Date
    22. 3.2008 14:46:06
  7. Sánchez, D.; Chamorro-Martínez, J.; Vila, M.A.: Modelling subjectivity in visual perception of orientation for image retrieval (2003) 0.04 
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    Abstract
    In this paper we combine computer vision and data mining techniques to model high-level concepts for image retrieval, on the basis of basic perceptual features of the human visual system. High-level concepts related to these features are learned and represented by means of a set of fuzzy association rules. The concepts so acquired can be used for image retrieval with the advantage that it is not needed to provide an image as a query. Instead, a query is formulated by using the labels that identify the learned concepts as search terms, and the retrieval process calculates the relevance of an image to the query by an inference mechanism. An additional feature of our methodology is that it can capture user's subjectivity. For that purpose, fuzzy sets theory is employed to measure user's assessments about the fulfillment of a concept by an image.
    Source
    Information processing and management. 39(2003) no.2, S.251-266
  8. Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000) 0.03 
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    Abstract
    In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin
  9. Liu, Y.; Huang, X.; An, A.: Personalized recommendation with adaptive mixture of markov models (2007) 0.03 
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    Abstract
    With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink.
  10. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.03 
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    Abstract
    Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it
    Date
    22.11.1998 18:57:22
  11. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.03 
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    Abstract
    20th century massification of higher education and research in academia is said to have produced structurally stratified higher education systems in many countries. Most manifestly, the research mission of universities appears to be divisive. Authors have claimed that the Swedish system, while formally unified, has developed into a binary state, and statistics seem to support this conclusion. This article makes use of a comprehensive statistical data source on Swedish higher education institutions to illustrate stratification, and uses literature on Swedish research policy history to contextualize the statistics. Highlighting the opportunities as well as constraints of the data, the article argues that there is great merit in combining statistics with a qualitative analysis when studying the structural characteristics of national higher education systems. Not least the article shows that it is an over-simplification to describe the Swedish system as binary; the stratification is more complex. On basis of the analysis, the article also argues that while global trends certainly influence national developments, higher education systems have country-specific features that may enrich the understanding of how systems evolve and therefore should be analyzed as part of a broader study of the increasingly globalized academic system.
    Date
    22. 3.2013 19:43:01
  12. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.03 
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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
    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  13. Lischka, K.: Spurensuche im Datenwust : Data-Mining-Software fahndet nach kriminellen Mitarbeitern, guten Kunden - und bald vielleicht auch nach Terroristen (2002) 0.02 
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    Content
    "Ob man als Terrorist einen Anschlag gegen die Vereinigten Staaten plant, als Kassierer Scheine aus der Kasse unterschlägt oder für bestimmte Produkte besonders gerne Geld ausgibt - einen Unterschied macht Data-Mining-Software da nicht. Solche Programme analysieren riesige Daten- mengen und fällen statistische Urteile. Mit diesen Methoden wollen nun die For- scher des "Information Awaren in den Vereinigten Staaten Spuren von Terroristen in den Datenbanken von Behörden und privaten Unternehmen wie Kreditkartenfirmen finden. 200 Millionen Dollar umfasst der Jahresetat für die verschiedenen Forschungsprojekte. Dass solche Software in der Praxis funktioniert, zeigen die steigenden Umsätze der Anbieter so genannter Customer-Relationship-Management-Software. Im vergangenen Jahr ist das Potenzial für analytische CRM-Anwendungen laut dem Marktforschungsinstitut IDC weltweit um 22 Prozent gewachsen, bis zum Jahr 2006 soll es in Deutschland mit einem jährlichen Plus von 14,1 Prozent so weitergehen. Und das trotz schwacher Konjunktur - oder gerade deswegen. Denn ähnlich wie Data-Mining der USRegierung helfen soll, Terroristen zu finden, entscheiden CRM-Programme heute, welche Kunden für eine Firma profitabel sind. Und welche es künftig sein werden, wie Manuela Schnaubelt, Sprecherin des CRM-Anbieters SAP, beschreibt: "Die Kundenbewertung ist ein zentraler Bestandteil des analytischen CRM. Sie ermöglicht es Unternehmen, sich auf die für sie wichtigen und richtigen Kunden zu fokussieren. Darüber hinaus können Firmen mit speziellen Scoring- Verfahren ermitteln, welche Kunden langfristig in welchem Maße zum Unternehmenserfolg beitragen." Die Folgen der Bewertungen sind für die Betroffenen nicht immer positiv: Attraktive Kunden profitieren von individuellen Sonderangeboten und besonderer Zuwendung. Andere hängen vielleicht so lauge in der Warteschleife des Telefonservice, bis die profitableren Kunden abgearbeitet sind. So könnte eine praktische Umsetzung dessen aussehen, was SAP-Spreche-rin Schnaubelt abstrakt beschreibt: "In vielen Unternehmen wird Kundenbewertung mit der klassischen ABC-Analyse durchgeführt, bei der Kunden anhand von Daten wie dem Umsatz kategorisiert werden. A-Kunden als besonders wichtige Kunden werden anders betreut als C-Kunden." Noch näher am geplanten Einsatz von Data-Mining zur Terroristenjagd ist eine Anwendung, die heute viele Firmen erfolgreich nutzen: Sie spüren betrügende Mitarbeiter auf. Werner Sülzer vom großen CRM-Anbieter NCR Teradata beschreibt die Möglichkeiten so: "Heute hinterlässt praktisch jeder Täter - ob Mitarbeiter, Kunde oder Lieferant - Datenspuren bei seinen wirtschaftskriminellen Handlungen. Es muss vorrangig darum gehen, einzelne Spuren zu Handlungsmustern und Täterprofilen zu verdichten. Das gelingt mittels zentraler Datenlager und hoch entwickelter Such- und Analyseinstrumente." Von konkreten Erfolgen sprich: Entlas-sungen krimineller Mitarbeiter-nach Einsatz solcher Programme erzählen Unternehmen nicht gerne. Matthias Wilke von der "Beratungsstelle für Technologiefolgen und Qualifizierung" (BTQ) der Gewerkschaft Verdi weiß von einem Fall 'aus der Schweiz. Dort setzt die Handelskette "Pick Pay" das Programm "Lord Lose Prevention" ein. Zwei Monate nach Einfüh-rung seien Unterschlagungen im Wert von etwa 200 000 Franken ermittelt worden. Das kostete mehr als 50 verdächtige Kassiererinnen und Kassierer den Job.
  14. Survey of text mining : clustering, classification, and retrieval (2004) 0.02 
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    Abstract
    Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
    Classification
    ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme
    RVK
    ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme
  15. Data mining : Theoretische Aspekte und Anwendungen (1998) 0.02 
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    Theme
    Information Resources Management
  16. Analytische Informationssysteme : Data Warehouse, On-Line Analytical Processing, Data Mining (1998) 0.02 
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    Theme
    Information Resources Management
  17. Information visualization in data mining and knowledge discovery (2002) 0.02 
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    Date
    23. 3.2008 19:10:22
    Footnote
    In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
    Series
    Morgan Kaufmann series in data management systems
  18. Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003) 0.02 
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    Date
    22. 4.2003 11:45:36
  19. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.02 
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    Date
    2. 4.2000 18:01:22
  20. Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002) 0.02 
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    Abstract
    Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.
    Content
    Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.

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  • e 35
  • d 10

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  • a 32
  • m 11
  • s 11
  • el 2
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