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1Fayyad, U. et al. (Hrsg.): Information visualization in data mining and knowledge discovery.
San Francisco, CA : Morgan Kaufmann Publ., 2002. xiii, 407 S.
(Morgan Kaufmann series in data management systems)
Anmerkung: Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems. ; 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. ; With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."
Themenfeld: Data Mining ; Visualisierung
LCSH: Information visualization ; Data mining ; Knowledge acquisition (Expert systems)
RSWK: Visualisierung / Computergraphik / Data Mining ; Information Retrieval (BVB) ; Visualisierung (BVB) ; Wissensextraktion (BVB) ; Lehrbuch (BVB) ; Data Mining / Visualisierung / Aufsatzsammlung (BVB) ; Wissensextraktion / Visualisierung / Aufsatzsammlung (BVB)
BK: 54.72 / Künstliche Intelligenz ; 54.73 / Computergraphik ; 54.74 / Maschinelles Sehen ; 06.74 / Informationssysteme
DDC: 006.3 / dc21
GHBS: TYX (DU) ; PZY (DU) ; QGT (DU) ; TWY (DU) ; TYR (HA) ; TYP (HA) ; TZD (HA)
LCC: TK7882.I6I635 2002
2Barnsley, M.F. ; Hurd, L.P.: Bildkompression mit Fraktalen.Übers. und bearb. von Gisbert W. Selke und Harald Selke.
Braunschweig : Vieweg, 1996. VIII, 232 S.
Anmerkung: Originaltitel: Fractal image compression
LCSH: Image processing / Digital techniques / Mathematics ; Fractals ; Image compression
RSWK: Bildverarbeitung / Datenkompression ; Fraktal (BVB) ; Bildverarbeitung / Datenkompression / Fraktal (BVB) ; Fraktal / Datenkompression (BVB)
BK: 54.74 / Maschinelles Sehen ; 31.59 / Geometrie: Sonstiges
DDC: 621.36/7 / dc20
Eppelsheimer: Mat J 91 / Fraktal ; Mat T 1068 / Bildverarbeitung ; Mat T 1087 / Datenreduktion
GHBS: TYX (DU) ; TVV (DU) ; YCB (DU) ; THL (PB) ; TVVC (SI) ; TZD (HA) ; TGJ (HA) ; SWL (HA) ; TZG (HA)
LCC: TA1632.B353 1993
RVK: UG 3900 Physik / Thermodynamik, statistische Physik, Quantenstatistik / Synergetik, Kooperative und Kollektive Phänomene, spontane Ordnungsprozesse, Verzweigungsprozesse, Chaotische Prozesse, Fraktale, Selbstorganisierende Systeme, Kritische Phänomene ; ST 330 Informatik / Monographien / Einzelne Anwendungen der Datenverarbeitung / Bildverarbeitung und Mustererkennung ; ST 300 Informatik / Monographien / Künstliche Intelligenz / Allgemeines ; ZN 6050 Technik / Elektrotechnik, Elektronik, Nachrichtentechnik / Nachrichtentechnik; Telekommunikation / Mustererkennung; Bilderkennung; Bildverarbeitung (Anwendung in der Nachrichtentechnik) ; ZN 6055 Technik / Elektrotechnik, Elektronik, Nachrichtentechnik / Nachrichtentechnik; Telekommunikation / Datenverdichtung in Bildverarbeitung und Multimedia (z.B. mpeg)