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Author: Daniel Barbara
Publisher: SIAM
Published: 2003-01-01
Total Pages: 368
ISBN-13: 9780898715453
DOWNLOAD EBOOKThe third SIAM International Conference on Data Mining provided an open forum for the presentation, discussion and development of innovative algorithms, software and theories for data mining applications and data intensive computation. This volume includes 21 research papers.
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Publisher:
Published: 2005
Total Pages: 674
ISBN-13:
DOWNLOAD EBOOKAuthor: Michael W. Berry
Publisher: SIAM
Published: 2004-01-01
Total Pages: 556
ISBN-13: 9780898715682
DOWNLOAD EBOOKThe Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.
Author: Chid Apte
Publisher: Proceedings in Applied Mathema
Published: 2007
Total Pages: 674
ISBN-13:
DOWNLOAD EBOOKThe Seventh SIAM International Conference on Data Mining (SDM 2007) continues a series of conferences whose focus is the theory and application of data mining to complex datasets in science, engineering, biomedicine, and the social sciences. These datasets challenge our abilities to analyze them because they are large and often noisy. Sophisticated, highperformance, and principled analysis techniques and algorithms, based on sound statistical foundations, are required. Visualization is often critically important; tuning for performance is a significant challenge; and the appropriate levels of abstraction to allow end-users to exploit sophisticated techniques and understand clearly both the constraints and interpretation of results are still something of an open question.
Author: Hillol Kargupta
Publisher: SIAM
Published: 2005-04-01
Total Pages: 670
ISBN-13: 9780898715934
DOWNLOAD EBOOKThe Fifth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. Advances in information technology and data collection methods have led to the availability of large data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines. The field of data mining draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high performance computing to discover interesting and previously unknown information in data. This conference results in data mining, including applications, algorithms, software, and systems.
Author: Joydeep Ghosh
Publisher: SIAM
Published: 2006-04-01
Total Pages: 662
ISBN-13: 9780898716115
DOWNLOAD EBOOKThe Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.
Author: Deepayan Chakrabarti
Publisher: Morgan & Claypool Publishers
Published: 2012-10-01
Total Pages: 209
ISBN-13: 160845116X
DOWNLOAD EBOOKWhat does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions
Author: Jaideep Vaidya
Publisher: Springer Science & Business Media
Published: 2006-09-28
Total Pages: 124
ISBN-13: 0387294899
DOWNLOAD EBOOKPrivacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.
Author: Richard Ellis
Publisher: Springer Science & Business Media
Published: 2009-10-28
Total Pages: 504
ISBN-13: 1848829833
DOWNLOAD EBOOKThe most common document formalisation for text classi?cation is the vector space model founded on the bag of words/phrases representation. The main advantage of the vector space model is that it can readily be employed by classi?cation - gorithms. However, the bag of words/phrases representation is suited to capturing only word/phrase frequency; structural and semantic information is ignored. It has been established that structural information plays an important role in classi?cation accuracy [14]. An alternative to the bag of words/phrases representation is a graph based rep- sentation, which intuitively possesses much more expressive power. However, this representation introduces an additional level of complexity in that the calculation of the similarity between two graphs is signi?cantly more computationally expensive than between two vectors (see for example [16]). Some work (see for example [12]) has been done on hybrid representations to capture both structural elements (- ing the graph model) and signi?cant features using the vector model. However the computational resources required to process this hybrid model are still extensive.
Author: Erickson, John
Publisher: IGI Global
Published: 2009-02-28
Total Pages: 2962
ISBN-13: 1605660590
DOWNLOAD EBOOK"This reference expands the field of database technologies through four-volumes of in-depth, advanced research articles from nearly 300 of the world's leading professionals"--Provided by publisher.
