Sat, 05 Jul 2008 13:17:26 BST CiteULike: fsilvestri's library [29 articles] http://www.citeulike.org/user/fsilvestri CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/fsilvestri/article/232154 <i>(2001), pp. 51-58.</i> Rank-preserving two-level caching for scalable search engines Paricia Saraiva Edleno de Moura Novio Ziviani Wagner Meira Rodrigo Fonseca Berthier Riberio-Neto doi:10.1145/383952.383959 (2001), pp. 51-58. 2005-06-20T01:31:37-00:00 2001 51 58 ACM Press caching http://www.citeulike.org/user/fsilvestri/article/232155 <i>(2005), pp. 257-266.</i> Three-level caching for efficient query processing in large Web search engines Xiaohui Long Torsten Suel doi:10.1145/1060745.1060785 (2005), pp. 257-266. 2005-06-20T01:32:53-00:00 2005 257 266 ACM Press caching http://www.citeulike.org/user/fsilvestri/article/287735 <i>(30 August 2005)</i> Data Mining For Bioinformatics Sumeet Dua (30 August 2005) 2005-08-15T17:59:19-00:00 2005 Crc Pr I Llc algorithms analysis bioinformatics data mining http://www.citeulike.org/user/fsilvestri/article/287734 <i>(16 October 2004)</i><br /><br /><P>This book looks at the mathematical foundations of the models which is crucial for correct interpretation of the outputs of the models. A bioinformatician should be able not only use software packages, but also know the mathematics behind these packages. From this point of view, mathematics departments throughout the world have a major role to play in bioinformatics education by teaching courses on the mathematical foundations of bioinformatics. The author wrote this book based on his lecture notes for his courses. It combines several topics in biological sequence analysis with mathematical and statistical material required for such analysis.</P> Introduction To Mathematical Methods In Bioinformatics (Universitext) Alexander Isaev (16 October 2004) 2005-08-15T17:58:47-00:00 2004 Springer analysis bioinformatics models http://www.citeulike.org/user/fsilvestri/article/287733 <i>(31 January 2003)</i><br /><br />Introduction to Bioinformatics: A Theoretical and Practical Approach was written as an introductory text for the undergraduate, graduate, or professional. This text provides scientists with both a biological framework to understand the questions life scientist confront in the context of the computational issues and tools that are currently available for scientific research It also provides the life scientist with a resource to the various computational tools that are available all supported with their underlying mathematical foundations. The book is divided into four main sections. The first two sections provide an overview of the various biological processes that govern an organism and impact health. The first section, Biochemistry, Cell and Molecular Biology, describes basic cellular structure and the decoding of the genome. The second section, Molecular Genetics covers the regulation of genomes and the molecular genetic basis of disease as a consequence of genetic replication. Clinical human genetics and the various clinical databases are also reviewed. The third section, the Unix Operating System, demystifies the Unix system used throughout the world to support advanced computation tools. In addition to information on the installation and management of Unix-based software tools, examples of command line sequence analyses are presented that will enable the research to become as comfortable in a command-line environment as they are in the Graphical-User Interface environment. The final section, Computer Applications, provides information on the management and analysis of DNA sequencing projects, along with a review of how DNA can be modeled as a statistical series of patterns. It follows with a discussion of the various genome databases, the representation of genomes, and methods for their large scale analyses. Protein visualization, and transcription profiling including the use of analysis software for systems biology round out the coverage. The volume also includes a bonus CD-ROM containing valuable software programs including BioDiscovery (for microarray analysis), ClustalX (a sequence alignment program) Ensembl, MicroAnalyser (for microarray analysis on the Macintosh), Staden Sequence Analysis Package, Tree View (for displaying phylogenies) an others. Also included is a complete set of color illustrations from each chapter that will prove invaluable for professors preparing their next bioinformatics course or seminar. Introduction to Bioinformatics: A Theoretical and Practical Approach Stephen Krawetz (31 January 2003) 2005-08-15T17:58:21-00:00 2003 Humana Press bioinformatics http://www.citeulike.org/user/fsilvestri/article/287732 <i>(17 September 2004)</i><br /><br />The goal of this book is to help readers understand state-of-the-art techniques in biological data mining and data management and includes topics such as: - preprocessing tasks such as data cleaning and data integration as applied to biological data - classification and clustering techniques for microarrays - comparison of RNA structures based on string properties and energetics - discovery of the sequence characteristics of different parts of the genome - mining of haplotypes to find disease markers - sequencing of events leading to the folding of a protein - inference of the subcellular location of protein activity - classification of chemical compounds based on structure - special purpose metrics and index structures for phylogenetic applications - a new query language for protein searching based on the shape of proteins - very fast indexing schemes for sequences and  pathways Aimed at computer scientists, necessary biology is explained. Data Mining in Bioinformatics (Advanced Information and Knowledge Processing) Jason Wang Mohammed Zaki Hannu Toivonen (17 September 2004) 2005-08-15T17:57:55-00:00 2004 Springer bioinformatics data mining http://www.citeulike.org/user/fsilvestri/article/98374 <i>Journal of Intelligent Information Systems, Vol. 24, No. 1. (January 2005), pp. 29-60.</i> Generating a Condensed Representation for Association Rules Nicolas Pasquier Rafik Taouil Yves Bastide Gerd Stumme Lotfi Lakhal doi:10.1007/s10844-005-0266-z Journal of Intelligent Information Systems, Vol. 24, No. 1. (January 2005), pp. 29-60. 2005-02-18T13:22:01-00:00 2005 Journal of Intelligent Information Systems 0925-9902 24 1 29 60 Kluwer Academic Publishers association mining rules http://www.citeulike.org/user/fsilvestri/article/226521 <i>(October 2000), pp. 158-167.</i> Analysis of recommendation algorithms for e-commerce Badrul Sarwar George Karypis Joseph Konstan John Riedl (October 2000), pp. 158-167. 2005-06-12T09:30:55-00:00 2000 158 167 ACM data mining recommender systems http://www.citeulike.org/user/fsilvestri/article/922 <i>Computer Networks and ISDN Systems, Vol. 30, No. 1--7. (1998), pp. 107-117.</i><br /><br />In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at The anatomy of a large-scale hypertextual Web search engine Sergey Brin Lawrence Page Computer Networks and ISDN Systems, Vol. 30, No. 1--7. (1998), pp. 107-117. 2004-11-22T17:49:28-00:00 1998 Computer Networks and ISDN Systems 30 1--7 107 117 algorithms analysis data engine link mining search web http://www.citeulike.org/user/fsilvestri/article/221107 <i>Information Processing Letters, Vol. 76, No. 4--6. (2000), pp. 175-181.</i><br /><br />We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A clustering algorithm based on graph connectivity Erez Hartuv Ron Shamir Information Processing Letters, Vol. 76, No. 4--6. (2000), pp. 175-181. 2005-06-06T20:55:33-00:00 2000 Information Processing Letters 76 4--6 175 181 clustering graph mining partitioning http://www.citeulike.org/user/fsilvestri/article/246445 <i>(2004), pp. 118-127.</i> Fast discovery of connection subgraphs Christos Faloutsos Kevin Mccurley Andrew Tomkins doi:10.1145/1014052.1014068 (2004), pp. 118-127. 2005-07-05T18:44:22-00:00 2004 118 127 ACM Press no-tag http://www.citeulike.org/user/fsilvestri/article/267625 <i>SIGMOD Rec., Vol. 32, No. 2. (June 2003), pp. 5-14.</i> Issues in data stream management Lukasz Golab Tamer &\#214;zsu doi:10.1145/776985.776986 SIGMOD Rec., Vol. 32, No. 2. (June 2003), pp. 5-14. 2005-07-29T11:33:28-00:00 2003 SIGMOD Rec. 0163-5808 32 2 5 14 ACM Press algorithms analysis data patterns streaming theory http://www.citeulike.org/user/fsilvestri/article/267621 <i>(2003), pp. 30-39.</i> Better streaming algorithms for clustering problems Moses Charikar Liadan O'Callaghan Rina Panigrahy doi:10.1145/780542.780548 (2003), pp. 30-39. 2005-07-29T11:27:50-00:00 2003 30 39 ACM Press algorithms clustering data mining streaming http://www.citeulike.org/user/fsilvestri/article/221093 <i>(1998)</i><br /><br />The graph partitioning problem is as follows. Given a graph G = (N; E) (where N is a set of weighted nodes and E is a set of weighted edges) and a positive integer p, find p subsets N 1 ; N 2 ; : : : ; N p of N such that 1. [ p i=1 N i = N and N i " N j = ; for i 6= j, 2. W (i) W=p, i = 1; 2; : : : ; p, where W (i) and W are the sums of the node weights in N i and N , respectively, 3. the cut size, i.e., the sum of weights of edges crossing between subsets is minimized. This problem ... Algorithms for graph partitioning: A survey P Fjallstrom (1998) 2005-06-06T20:35:36-00:00 1998 graph partitioning http://www.citeulike.org/user/fsilvestri/article/267614 <i>(1996)</i><br /><br />this article we give an introduction to the theory of online algorithms and survey interesting application areas. We present important results and outline directions for future research. Introduction Competitive online algorithms S Albers (1996) 2005-07-29T11:19:12-00:00 1996 algorithms analysis on-line theory http://www.citeulike.org/user/fsilvestri/article/267613 <i>(2004), pp. 197-205.</i> Web usage mining based on probabilistic latent semantic analysis Xin Jin Yanzan Zhou Bamshad Mobasher doi:10.1145/1014052.1014076 (2004), pp. 197-205. 2005-07-29T11:15:49-00:00 2004 197 205 ACM Press algorithms data mining web http://www.citeulike.org/user/fsilvestri/article/267593 <i>(01 September 1998)</i><br /><br />This coherent anthology presents the state of the art in the booming area of online algorithms and competitive analysis of such algorithms. The 17 papers are carefully revised and thoroughly improved versions of presentations given first during a Dagstuhl seminar in 1996. An overview by the volume editors introduces the area to the reader. The technical chapters are devoted to foundational and methodological issues for the design and analysis of various classes of online algorithms as well as to the detailed evaluation of algorithms for various activities in online processing, ranging from load balancing and scheduling to networking and financial problems. An outlook by the volume editors and a bibliography listing more than 750 references complete the work. The book is ideally suited for advanced courses and self-study in online algorithms. It is indispensable reading for researchers and professionals active in the area. Online Algorithms: The State of the Art (Lecture Notes in Computer Science) Amos Fiat (01 September 1998) 2005-07-29T10:01:59-00:00 1998 Springer algorithms analysis on-line theory http://www.citeulike.org/user/fsilvestri/article/171426 <i>Knowledge and Data Engineering, IEEE Transactions on, Vol. 17, No. 6. (2005), pp. 734-749.</i><br /><br />This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions G Adomavicius A Tuzhilin Knowledge and Data Engineering, IEEE Transactions on, Vol. 17, No. 6. (2005), pp. 734-749. 2005-04-26T12:49:12-00:00 2005 Knowledge and Data Engineering, IEEE Transactions on 17 6 734 749 algorithms analysis mining personalization recommender systems web http://www.citeulike.org/user/fsilvestri/article/111664 <i>(15 August 2002)</i><br /><br />Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort.<br><br>* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.<br>* Details the special challenges associated with analyzing unstructured and semi-structured data.<br>* Looks at how classical Information Retrieval techniques have been modified for use with Web data.<br>* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.<br>* Analyzes current applications for resource discovery and social network analysis.<br>* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.</li></ul> Mining the Web: Analysis of Hypertext and Semi Structured Data Soumen Chakrabarti (15 August 2002) 2005-03-02T15:59:19-00:00 2002 Morgan Kaufmann algorithms analysis mining web http://www.citeulike.org/user/fsilvestri/article/201727 <i>(01 September 2001)</i> Introduction to Algorithms, Second Edition Thomas Cormen Charles Leiserson Ronald Rivest Clifford Stein (01 September 2001) 2005-05-17T00:06:11-00:00 2001 The MIT Press algorithms theory http://www.citeulike.org/user/fsilvestri/article/267589 <i>(06 September 2000)</i><br /><br />Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases.<br><br>Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success. <br><br>Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms.<br><br>Classroom Features Available Online:<br>- instructor's manual<br>- course slides (in PowerPoint)<br>- course supplementary readings<br>- sample assignments and course projects<br><br>* Offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.<br>* Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn.<br>* Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.<br>* Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis.<br>* Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) Jiawei Han Micheline Kamber (06 September 2000) 2005-07-29T09:22:35-00:00 2000 Morgan Kaufmann data mining http://www.citeulike.org/user/fsilvestri/article/267320 <i>J. ACM, Vol. 51, No. 5. (September 2004), pp. 731-779.</i> Automatic information extraction from large websites Valter Crescenzi Giansalvatore Mecca doi:10.1145/1017460.1017462 J. ACM, Vol. 51, No. 5. (September 2004), pp. 731-779. 2005-07-28T14:20:35-00:00 2004 J. ACM 0004-5411 51 5 731 779 ACM Press algorithms data mining web http://www.citeulike.org/user/fsilvestri/article/267302 <i>ACM Trans. Database Syst., Vol. 30, No. 2. (June 2005), pp. 333-363.</i> Tight upper bounds on the number of candidate patterns Floris Geerts Bart Goethals Jan Van Den Bussche doi:10.1145/1071610.1071611 ACM Trans. Database Syst., Vol. 30, No. 2. (June 2005), pp. 333-363. 2005-07-28T14:17:00-00:00 2005 ACM Trans. Database Syst. 0362-5915 30 2 333 363 ACM Press bounds data frequent mining patterns upper http://www.citeulike.org/user/fsilvestri/article/267301 <i>ACM Trans. Inter. Tech., Vol. 5, No. 1. (February 2005), pp. 231-297.</i> Link analysis ranking: algorithms, theory, and experiments Allan Borodin Gareth Roberts Jeffrey Rosenthal Panayiotis Tsaparas doi:10.1145/1052934.1052942 ACM Trans. Inter. Tech., Vol. 5, No. 1. (February 2005), pp. 231-297. 2005-07-28T14:13:57-00:00 2005 ACM Trans. Inter. Tech. 1533-5399 5 1 231 297 ACM Press algorithms analysis engine experiments link ranking search theory web http://www.citeulike.org/user/fsilvestri/article/267300 <i>ACM Trans. Inter. Tech., Vol. 5, No. 2. (May 2005), pp. 299-327.</i> Moderately hard, memory-bound functions Martin Abadi Mike Burrows Mark Manasse Ted Wobber doi:10.1145/1064340.1064341 ACM Trans. Inter. Tech., Vol. 5, No. 2. (May 2005), pp. 299-327. 2005-07-28T14:12:40-00:00 2005 ACM Trans. Inter. Tech. 1533-5399 5 2 299 327 ACM Press no-tag http://www.citeulike.org/user/fsilvestri/article/267298 <i>ACM Trans. Inf. Syst., Vol. 23, No. 1. (January 2005), pp. 103-145.</i> Incorporating contextual information in recommender systems using a multidimensional approach Gediminas Adomavicius Ramesh Sankaranarayanan Shahana Sen Alexander Tuzhilin doi:10.1145/1055709.1055714 ACM Trans. Inf. Syst., Vol. 23, No. 1. (January 2005), pp. 103-145. 2005-07-28T14:09:20-00:00 2005 ACM Trans. Inf. Syst. 1046-8188 23 1 103 145 ACM Press collaborative context-aware data estimation filtering models multidimensional personalization rating recommender systems http://www.citeulike.org/user/fsilvestri/article/267296 <i>ACM Trans. Inf. Syst., Vol. 23, No. 1. (January 2005), pp. 1-2.</i> Introduction to genomic information retrieval Hugh Williams doi:10.1145/1055709.1055710 ACM Trans. Inf. Syst., Vol. 23, No. 1. (January 2005), pp. 1-2. 2005-07-28T14:08:10-00:00 2005 ACM Trans. Inf. Syst. 1046-8188 23 1 1 2 ACM Press no-tag http://www.citeulike.org/user/fsilvestri/article/267295 <i>ACM Trans. Inf. Syst., Vol. 23, No. 2. (April 2005), pp. 169-200.</i> Ad Hoc, self-supervising peer-to-peer search networks Brian Cooper Hector Garcia-Molina doi:10.1145/1059981.1059983 ACM Trans. Inf. Syst., Vol. 23, No. 2. (April 2005), pp. 169-200. 2005-07-28T14:06:23-00:00 2005 ACM Trans. Inf. Syst. 1046-8188 23 2 169 200 ACM Press ad-hoc peer-to-peer search http://www.citeulike.org/user/fsilvestri/article/267291 <i>(2004), pp. 84-92.</i> New results on web caching with request reordering Susanne Albers doi:10.1145/1007912.1007925 (2004), pp. 84-92. 2005-07-28T13:59:23-00:00 2004 84 92 ACM Press caching reordering request servers web