Sistem Informasi Pustaka

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of data mining and knowledge discovery. This year marks the tenth anniversary of the successful annual series of PAKDD conferences held in the Asia Pacific region. It was with pleasure that we hosted PAKDD 2006 in Singapore again, since the inaugural PAKDD conference was held in Singapore in 1997. PAKDD 2006 continues its tradition of providing an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all aspects of KDD data mining, including data cleaning, data warehousing, data mining techniques, knowledge visualization, and data mining applications. This year, we received 501 paper submissions from 38 countries and regions in Asia, Australasia, North America and Europe, of which we accepted 67 (13.4%) papers as regular papers and 33 (6.6%) papers as short papers. The distribution of the accepted papers was as follows: USA (17%), China (16%), Taiwan (10%), Australia (10%), Japan (7%), Korea (7%), Germany (6%), Canada (5%), Hong Kong (3%), Singapore (3%), New Zealand (3%), France (3%), UK (2%), and the rest from various countries in the Asia Pacific region.

Wee Keong Ng,

Mining. for. Surveillance. Applications. Bhavani M. Thuraisingham1,2 1 Eric
Jonsson School of Engineering and Computer Science, University of Texas at
Dallas, Richardson, Texas 75083-0688, USA ...

Principles of Data Mining

Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This should prove of value to readers of all kinds, from those whose only use of data mining techniques will be via commercial packages right through to academic researchers. This book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.

Max Bramer,

Data for data mining comes in many forms: from computer files typed in by human
operators, business information in SQL or some other standard database format,
information recorded automatically by equipment such as fault logging devices, ...

Advanced Data Mining and Applications

Second International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, Proceedings

Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.

Xue Li, Osmar R. Zaiane, Zhanhuai Li,

Second International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006,
Proceedings Xue Li, Osmar R. Zaiane, Zhanhuai Li. Activity Mining: Challenges
and Prospects* Longbing Cao Faculty of Information Technology, University of ...

Data Mining in Agriculture

Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.

Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos,

This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.

Next Generation of Data Mining

Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Hillol Kargupta, Jiawei Han, Philip S. Yu, Rajeev Motwani, Vipin Kumar,

15 1.2.9 Domain-Specific Data Mining: Data Mining by Integration of
Sophisticated Scientific and Engineering Domain Knowledge . . . . . . . . 17 1.2.9.1
BiologicalDataMining ........................................ 17 1.2.9.2
DataMiningforSoftwareEngineering.

Machine Learning and Data Mining in Pattern Recognition

6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

Petra Perner,

Mining Lung Shape from X-Ray Images Vassili Kovalev1,Aliaksandr Prus1, and
PavelVankevich2 1 Biomedical Image Analysis Group, United Institute of
Informatics Problems, National Academy of Sciences of Belarus Room 803,
Kirova St., ...

Data Mining in Biomedicine

This volume presents an extensive collection of contributions covering aspects of the exciting and important research field of data mining techniques in biomedicine. Coverage includes new approaches for the analysis of biomedical data; applications of data mining techniques to real-life problems in medical practice; comprehensive reviews of recent trends in the field. The book addresses incorporation of data mining in fundamental areas of biomedical research: genomics, proteomics, protein characterization, and neuroscience.

Panos M. Pardalos, Vladimir L. Boginski, Alkis Vazacopoulos,

Mining. in. EEG: Application. to. Epileptic. Brain. Disorders*. P.M. Pardalos2, L.D.
Iasemidis3 , W. Suharitdamrong2, D.-S. Shiau2, L.K. Dance2, O.A. Prokopyev4,
V.L. Boginski5, PR. Carney2, and J.C. Sackellares2 1 Rutgers University, USA ...

Data Mining and Applications in Genomics

Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serves as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics.

Sio-Iong Ao,

This book is organized as follows. In this Chapter, it is the brief introduction to the
data mining algorithms, the advances in the technology and the outline of the
recent works for the genomic analysis. In the last section, we describe briefly
about ...

Advanced Data Mining and Applications

5th International Conference, ADMA 2009, Chengdu, China, August 17-19, 2009, Proceedings

This book constitutes the refereed proceedings of the 5th International Conference on Advanced Data Mining and Applications, ADMA 2009, held in Beijing, China, in August 2009. The 34 revised full papers and 47 revised short papers presented together with the abstract of 4 keynote lectures were carefully reviewed and selected from 322 submissions from 27 countries. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining and feature original research results in data mining, spanning applications, algorithms, software and systems, and different applied disciplines with potential in data mining.

Ronghuai Huang, Qiang Yang, Jian Pei,

Mining. in. Financial. Markets. Longbing Cao Associate Professor, the University
of Technology, Sydney (UTS), Australia Abstract. The ongoing global financial
recession has dramatically affected public confidence and market development.

Managing Data Mining Technologies in Organizations

Techniques and Applications

Portals present unique strategic challenges in the academic environment. Their conceptualization and design requires the input of campus constituents who seldom interact and whose interests are often opposite. The implementation of a portal requires a coordination of applications and databases controlled by different campus units at a level that may never before have been attempted at the institution. Building a portal is as much about constructing intra-campus bridges as it is about user interfaces and content. Designing Portals: Opportunities and Challenges discusses the current status of portals in higher education by providing insight into the role portals play in an institution's business and educational strategy, by taking the reader through the processes of conceptualization, design, and implementation of the portals (in different stages of development) at major universities and by offering insight from three producers of portal software systems in use at institutions of higher learning and elsewhere.

Parag C. Pendharkar,

Chapter. 5. A. Proposed. Process. for. Performing. Data. Mining. Projects. KarimK.
Hirji IBM Canada Ltd., Canada ... The enthusiasm surrounding data mining at
large continues to grow; however, atthe same time, there are claims that data ...

Business Modeling and Data Mining

Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. · Teaches how to discover, construct and refine models that are useful in business situations · Teaches how to design, discover and develop the data necessary for mining · Provides a practical approach to mining data for all business situations · Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data · Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.

Dorian Pyle,

He explained that he worked in a bank, and also taught a graduate-level data
mining course. He asked if I had any advice about how to turn the business
description of a problem into a form that could be answered with data mining and
data.

Visual Data Mining

Theory, Techniques and Tools for Visual Analytics

The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected. This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.

Simeon Simoff, Michael H. Böhlen, Arturas Mazeika,

Form-Semantics-Function – A Framework for Designing Visual Data
Representations for Visual Data Mining Simeon J. Simoff School of Computing
and Mathematics College of Heath and Science University of Western Sydney
NSW 1797, ...

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