Search (27 results, page 1 of 2)

  • × theme_ss:"Data Mining"
  • × year_i:[2000 TO 2010}
  1. Medien-Informationsmanagement : Archivarische, dokumentarische, betriebswirtschaftliche, rechtliche und Berufsbild-Aspekte ; [Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar und Folgetagung 2001 in Köln] (2003) 0.06 
    0.057382297 = product of:
  0.08607344 = sum of:
    0.031938497 = weight(_text_:resources in 1833) [ClassicSimilarity], result of:
      0.031938497 = score(doc=1833,freq=4.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.17110959 = fieldWeight in 1833, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1833)
    0.054134943 = sum of:
      0.033351216 = weight(_text_:management in 1833) [ClassicSimilarity], result of:
        0.033351216 = score(doc=1833,freq=6.0), product of:
          0.17235184 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.051133685 = queryNorm
          0.19350658 = fieldWeight in 1833, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
      0.020783724 = weight(_text_:22 in 1833) [ClassicSimilarity], result of:
        0.020783724 = score(doc=1833,freq=2.0), product of:
          0.17906146 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.051133685 = queryNorm
          0.116070345 = fieldWeight in 1833, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
  0.6666667 = coord(2/3)
    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.
    Date
    11. 5.2008 19:49:22
    LCSH
    Mass media / Archival resources / Congresses
    Information technology / Management / Congresses
    Subject
    Mass media / Archival resources / Congresses
    Information technology / Management / Congresses
  2. Relational data mining (2001) 0.06 
    0.055421554 = product of:
  0.08313233 = sum of:
    0.063876994 = weight(_text_:resources in 1303) [ClassicSimilarity], result of:
      0.063876994 = score(doc=1303,freq=4.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.34221917 = fieldWeight in 1303, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.046875 = fieldNorm(doc=1303)
    0.01925533 = product of:
      0.03851066 = sum of:
        0.03851066 = weight(_text_:management in 1303) [ClassicSimilarity], result of:
          0.03851066 = score(doc=1303,freq=2.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.22344214 = fieldWeight in 1303, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046875 = fieldNorm(doc=1303)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The ferst part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programmeng; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
    Theme
    Information Resources Management
  3. Liu, Y.; Huang, X.; An, A.: Personalized recommendation with adaptive mixture of markov models (2007) 0.04 
    0.035790663 = product of:
  0.053685993 = sum of:
    0.037639882 = weight(_text_:resources in 606) [ClassicSimilarity], result of:
      0.037639882 = score(doc=606,freq=2.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.20165458 = fieldWeight in 606, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0390625 = fieldNorm(doc=606)
    0.016046109 = product of:
      0.032092217 = sum of:
        0.032092217 = weight(_text_:management in 606) [ClassicSimilarity], result of:
          0.032092217 = score(doc=606,freq=2.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.18620178 = fieldWeight in 606, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0390625 = fieldNorm(doc=606)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    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.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  4. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.03 
    0.026692703 = product of:
  0.08007811 = sum of:
    0.08007811 = sum of:
      0.03851066 = weight(_text_:management in 1383) [ClassicSimilarity], result of:
        0.03851066 = score(doc=1383,freq=2.0), product of:
          0.17235184 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.051133685 = queryNorm
          0.22344214 = fieldWeight in 1383, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.046875 = fieldNorm(doc=1383)
      0.04156745 = weight(_text_:22 in 1383) [ClassicSimilarity], result of:
        0.04156745 = score(doc=1383,freq=2.0), product of:
          0.17906146 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.051133685 = queryNorm
          0.23214069 = fieldWeight in 1383, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=1383)
  0.33333334 = coord(1/3)
    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
  5. Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007) 0.02 
    0.024841055 = product of:
  0.074523166 = sum of:
    0.074523166 = weight(_text_:resources in 600) [ClassicSimilarity], result of:
      0.074523166 = score(doc=600,freq=4.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.39925572 = fieldWeight in 600, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0546875 = fieldNorm(doc=600)
  0.33333334 = coord(1/3)
    Footnote
    Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"
  6. Wang, F.L.; Yang, C.C.: Mining Web data for Chinese segmentation (2007) 0.02 
    0.017743612 = product of:
  0.053230833 = sum of:
    0.053230833 = weight(_text_:resources in 604) [ClassicSimilarity], result of:
      0.053230833 = score(doc=604,freq=4.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.28518265 = fieldWeight in 604, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0390625 = fieldNorm(doc=604)
  0.33333334 = coord(1/3)
    Abstract
    Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  7. Liu, Y.; Zhang, M.; Cen, R.; Ru, L.; Ma, S.: Data cleansing for Web information retrieval using query independent features (2007) 0.02 
    0.017743612 = product of:
  0.053230833 = sum of:
    0.053230833 = weight(_text_:resources in 607) [ClassicSimilarity], result of:
      0.053230833 = score(doc=607,freq=4.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.28518265 = fieldWeight in 607, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0390625 = fieldNorm(doc=607)
  0.33333334 = coord(1/3)
    Abstract
    Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  8. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.02 
    0.017565278 = product of:
  0.052695833 = sum of:
    0.052695833 = weight(_text_:resources in 601) [ClassicSimilarity], result of:
      0.052695833 = score(doc=601,freq=2.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.28231642 = fieldWeight in 601, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0546875 = fieldNorm(doc=601)
  0.33333334 = coord(1/3)
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  9. Perugini, S.; Ramakrishnan, N.: Mining Web functional dependencies for flexible information access (2007) 0.02 
    0.015055953 = product of:
  0.045167856 = sum of:
    0.045167856 = weight(_text_:resources in 602) [ClassicSimilarity], result of:
      0.045167856 = score(doc=602,freq=2.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.2419855 = fieldWeight in 602, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.046875 = fieldNorm(doc=602)
  0.33333334 = coord(1/3)
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  10. Schwartz, F.; Fang, Y.C.: Citation data analysis on hydrogeology (2007) 0.01 
    0.014194889 = product of:
  0.042584665 = sum of:
    0.042584665 = weight(_text_:resources in 433) [ClassicSimilarity], result of:
      0.042584665 = score(doc=433,freq=4.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.22814612 = fieldWeight in 433, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.03125 = fieldNorm(doc=433)
  0.33333334 = coord(1/3)
    Abstract
    This article explores the status of research in hydrogeology using data mining techniques. First we try to explain what citation analysis is and review some of the previous work on citation analysis. The main idea in this article is to address some common issues about citation numbers and the use of these data. To validate the use of citation numbers, we compare the citation patterns for Water Resources Research papers in the 1980s with those in the 1990s. The citation growths for highly cited authors from the 1980s are used to examine whether it is possible to predict the citation patterns for highly-cited authors in the 1990s. If the citation data prove to be steady and stable, these numbers then can be used to explore the evolution of science in hydrogeology. The famous quotation, "If you are not the lead dog, the scenery never changes," attributed to Lee Iacocca, points to the importance of an entrepreneurial spirit in all forms of endeavor. In the case of hydrogeological research, impact analysis makes it clear how important it is to be a pioneer. Statistical correlation coefficients are used to retrieve papers among a collection of 2,847 papers before and after 1991 sharing the same topics with 273 papers in 1991 in Water Resources Research. The numbers of papers before and after 1991 are then plotted against various levels of citations for papers in 1991 to compare the distributions of paper population before and after that year. The similarity metrics based on word counts can ensure that the "before" papers are like ancestors and "after" papers are descendants in the same type of research. This exercise gives us an idea of how many papers are populated before and after 1991 (1991 is chosen based on balanced numbers of papers before and after that year). In addition, the impact of papers is measured in terms of citation presented as "percentile," a relative measure based on rankings in one year, in order to minimize the effect of time.
  11. Lischka, K.: Spurensuche im Datenwust : Data-Mining-Software fahndet nach kriminellen Mitarbeitern, guten Kunden - und bald vielleicht auch nach Terroristen (2002) 0.01 
    0.013346352 = product of:
  0.040039055 = sum of:
    0.040039055 = sum of:
      0.01925533 = weight(_text_:management in 1178) [ClassicSimilarity], result of:
        0.01925533 = score(doc=1178,freq=2.0), product of:
          0.17235184 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.051133685 = queryNorm
          0.11172107 = fieldWeight in 1178, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1178)
      0.020783724 = weight(_text_:22 in 1178) [ClassicSimilarity], result of:
        0.020783724 = score(doc=1178,freq=2.0), product of:
          0.17906146 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.051133685 = queryNorm
          0.116070345 = fieldWeight in 1178, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1178)
  0.33333334 = coord(1/3)
    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.
  12. Shi, X.; Yang, C.C.: Mining related queries from Web search engine query logs using an improved association rule mining model (2007) 0.01 
    0.012546628 = product of:
  0.037639882 = sum of:
    0.037639882 = weight(_text_:resources in 597) [ClassicSimilarity], result of:
      0.037639882 = score(doc=597,freq=2.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.20165458 = fieldWeight in 597, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0390625 = fieldNorm(doc=597)
  0.33333334 = coord(1/3)
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  13. Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007) 0.01 
    0.012546628 = product of:
  0.037639882 = sum of:
    0.037639882 = weight(_text_:resources in 605) [ClassicSimilarity], result of:
      0.037639882 = score(doc=605,freq=2.0), product of:
        0.18665522 = queryWeight, product of:
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.051133685 = queryNorm
        0.20165458 = fieldWeight in 605, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.650338 = idf(docFreq=3122, maxDocs=44218)
          0.0390625 = fieldNorm(doc=605)
  0.33333334 = coord(1/3)
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
  14. Information visualization in data mining and knowledge discovery (2002) 0.01 
    0.010669973 = product of:
  0.03200992 = sum of:
    0.03200992 = sum of:
      0.018154101 = weight(_text_:management in 1789) [ClassicSimilarity], result of:
        0.018154101 = score(doc=1789,freq=4.0), product of:
          0.17235184 = queryWeight, product of:
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.051133685 = queryNorm
          0.10533164 = fieldWeight in 1789, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.3706124 = idf(docFreq=4130, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
      0.013855817 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
        0.013855817 = score(doc=1789,freq=2.0), product of:
          0.17906146 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.051133685 = queryNorm
          0.07738023 = fieldWeight in 1789, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
  0.33333334 = coord(1/3)
    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
  15. Survey of text mining : clustering, classification, and retrieval (2004) 0.01 
    0.009264226 = product of:
  0.027792677 = sum of:
    0.027792677 = product of:
      0.055585355 = sum of:
        0.055585355 = weight(_text_:management in 804) [ClassicSimilarity], result of:
          0.055585355 = score(doc=804,freq=6.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.32251096 = fieldWeight in 804, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0390625 = fieldNorm(doc=804)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    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
  16. 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) 0.01 
    0.007564209 = product of:
  0.022692626 = sum of:
    0.022692626 = product of:
      0.045385253 = sum of:
        0.045385253 = weight(_text_:management in 5997) [ClassicSimilarity], result of:
          0.045385253 = score(doc=5997,freq=4.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.2633291 = fieldWeight in 5997, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5997)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    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.
  17. Knowledge management in fuzzy databases (2000) 0.01 
    0.0074881846 = product of:
  0.022464553 = sum of:
    0.022464553 = product of:
      0.044929106 = sum of:
        0.044929106 = weight(_text_:management in 4260) [ClassicSimilarity], result of:
          0.044929106 = score(doc=4260,freq=2.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.2606825 = fieldWeight in 4260, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4260)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
  18. Liu, W.; Weichselbraun, A.; Scharl, A.; Chang, E.: Semi-automatic ontology extension using spreading activation (2005) 0.01 
    0.0074881846 = product of:
  0.022464553 = sum of:
    0.022464553 = product of:
      0.044929106 = sum of:
        0.044929106 = weight(_text_:management in 3028) [ClassicSimilarity], result of:
          0.044929106 = score(doc=3028,freq=2.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.2606825 = fieldWeight in 3028, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3028)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Source
    Journal of universal knowledge management. 0(2005) no.1, S.50-58
  19. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.01 
    0.0064184438 = product of:
  0.01925533 = sum of:
    0.01925533 = product of:
      0.03851066 = sum of:
        0.03851066 = weight(_text_:management in 2563) [ClassicSimilarity], result of:
          0.03851066 = score(doc=2563,freq=2.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.22344214 = fieldWeight in 2563, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046875 = fieldNorm(doc=2563)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Source
    Information processing and management. 40(2004) no.2, S.239-255
  20. Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003) 0.01 
    0.0064184438 = product of:
  0.01925533 = sum of:
    0.01925533 = product of:
      0.03851066 = sum of:
        0.03851066 = weight(_text_:management in 4242) [ClassicSimilarity], result of:
          0.03851066 = score(doc=4242,freq=2.0), product of:
            0.17235184 = queryWeight, product of:
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.051133685 = queryNorm
            0.22344214 = fieldWeight in 4242, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.3706124 = idf(docFreq=4130, maxDocs=44218)
              0.046875 = fieldNorm(doc=4242)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Abstract
    With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

Authors

Languages

  • e 22
  • d 5

Types

Themes

Subjects

Classifications