Download or read book Finding Frequent Patterns from Graph Datasets written by Michihiro Kuramochi and published by . This book was released on 2005 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mining Graph Data written by Diane J. Cook and published by John Wiley & Sons. This book was released on 2006-12-18 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.
Download or read book Frequent Pattern Mining written by Charu C. Aggarwal and published by Springer. This book was released on 2014-08-29 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
Download or read book New Techniques for Efficiently Discovering Frequent Patterns written by Ruoming Jin and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Because of its theoretical and practical importance, the field of frequent pattern mining has been and remain to be one of the most active research area in KDD. In this dissertation, we study three different problems in frequent pattern mining, mining multipledatasets, mining streaming data, and mining large-scale structures from graph datasets. Our study has not only extended the breadth of frequent pattern mining, but also brought new techniques and algorithms into this field. Specifically, our contributions are as follows. 1. Mining Multiple Datasets: We develop a systematic approach to generate efficient query plans for a single mining query across multiple datasets. We also propose methods to simultaneously optimize multiple such queries and utilize the past mining results in a query-intensive KDD environment. Our experimental results have shown a speedup up to two-order of magnitude comparing with the naive methods without these optimizations. 2. Mining Frequent Itemsets over Streaming Data: We propose a new algorithm StreamMining to discover the frequent itemsets over streaming data. In a single pass, StreamMining will guarantee to find a superset of frequent itemsets, but false positive may occur. If the second pass is allowed, StreamMining will be able to remove the false positive and find the exact frequent itemsets. Our detailed evaluation using both synthetic and real datasets has shown our one-pass algorithm is very accurate in practice, and is also very memory efficient. 3. Mining Frequent Large-Scale Structures from Graph Datasets: We develop a new framework to discover the frequent large-scale structures from graph datasets. This framework is derived from a mathematical concept, topological minor. In this framework, we propose a new algorithm TSMiner, which efficiently enumerates all the frequent large-scale structures in a graph dataset, and a new approach called relabeling function to perform constraint mining. We apply our framework to protein structure data and discover meaningful topological structures. Finally, we demonstrate the viability and scalability of the proposed algorithms on both real and synthetic datasets.
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Download or read book Graph Mining written by Deepayan Chakrabarti and published by Morgan & Claypool Publishers. This book was released on 2012-10-01 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: What 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
Download or read book Managing and Mining Graph Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-02-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.
Download or read book An Efficient Algorithm for Discovering Frequent Subgraphs written by and published by . This book was released on 2002 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the years, frequent itemset discovery algorithms have been used to find interesting patterns in various application areas. However, as data mining techniques are being increasingly applied to non-traditional domains, existing frequent pattern discovery approach cannot be used. This is because the transaction framework that is assumed by these algorithms cannot be used to effectively model the datasets in these domains. An alternate way of modeling the objects in these datasets is to represent them using graphs. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally efficient algorithm, called FSG, for finding all frequent subgraphs in large graph databases. We experimentally evaluate the performance of FSG using a variety of real and synthetic datasets. Our results show that despite the underlying complexity associated with frequent subgraph discovery, FSG is effective in finding all frequently occurring subgraphs in datasets containing over 100,000 graph transactions and scales linearly with respect to the size of the database.
Download or read book Graph Mining written by Deepayan Chakrabarti and published by Springer Nature. This book was released on 2022-05-31 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: What 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
Download or read book Intelligent Patterns Largedatabase Frequent written by Sheik Yousuf and published by Meem Publishers. This book was released on 2023-08-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent patterns frequent from large databases refers to the process of discovering meaningful and significant patterns or associations that occur frequently within vast datasets using intelligent data mining techniques. In data mining and pattern recognition, the term "frequent patterns" usually refers to items, sequences, or subsets that appear frequently in a given dataset. These patterns can provide valuable insights into the underlying relationships, trends, and behaviors within the data. Intelligent Patterns: These are meaningful and relevant patterns that are discovered using advanced algorithms and intelligent data analysis techniques. The intelligence here refers to the ability of the algorithms to identify patterns of interest and discard irrelevant or noise patterns. Frequent Patterns: These are patterns that occur frequently or have high support within the dataset. Support refers to the proportion of transactions or instances in which a particular pattern appears. Large Databases: Refers to datasets that are extensive and contain a significant amount of information. Large databases pose challenges for traditional data analysis methods, making intelligent data mining techniques crucial for effective pattern discovery. The process of finding intelligent frequent patterns from large databases typically involves using algorithms like Apriori, FP-Growth, or Eclat, which efficiently search for itemsets or sequences that meet predefined support and confidence thresholds. Applications of discovering frequent patterns include market basket analysis in retail (finding commonly purchased items together), web usage mining (finding frequently visited web pages), bioinformatics (finding frequent gene associations), and more. These patterns are valuable in decision-making, business intelligence, and predictive analytics, as they can reveal hidden relationships and trends within the data that might not be apparent through simple data examination.
Download or read book Proceedings of the Fourth SIAM International Conference on Data Mining written by Michael W. Berry and published by SIAM. This book was released on 2004-01-01 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 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.
Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Download or read book The Semantic Web Research and Applications written by Sean Bechhofer and published by Springer Science & Business Media. This book was released on 2008-05-20 with total page 916 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th European Semantic Web Conference, ESWC 2008, held in Tenerife, Canary Islands, Spain, in June 2008. The 51 revised full papers presented together with 3 invited talks and 25 system description papers were carefully reviewed and selected from a total of 270 submitted papers. The papers are organized in topical sections on agents, application ontologies, applications, formal languages, foundational issues, learning, ontologies and natural language, ontology alignment, query processing, search, semantic Web services, storage and retrieval of semantic Web data, as well as user interfaces and personalization.
Download or read book Concise Encyclopaedia of Bioinformatics and Computational Biology written by John M. Hancock and published by John Wiley & Sons. This book was released on 2014-06-02 with total page 1916 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concise Encyclopaedia of Bioinformatics and Computational Biology, 2nd Edition is a fully revised and updated version of this acclaimed resource. The book provides definitions and often explanations of over 1000 words, phrases and concepts relating to this fast-moving and exciting field, offering a convenient, one-stop summary of the core knowledge in the area. This second edition is an invaluable resource for students, researchers and academics.
Download or read book Proceedings of the 4th International Conference on Frontiers in Intelligent Computing Theory and Applications FICTA 2015 written by Swagatam Das and published by Springer. This book was released on 2015-10-24 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications 2015 (FICTA 2015) serves as the knowledge centre not only for scientists and researchers in the field of intelligent computing but also for students of post-graduate level in various engineering disciplines. The book covers a comprehensive overview of the theory, methods, applications and tools of Intelligent Computing. Researchers are now working in interdisciplinary areas and the proceedings of FICTA 2015 plays a major role to accumulate those significant works in one arena. The chapters included in the proceedings inculcates both theoretical as well as practical aspects of different areas like Nature Inspired Algorithms, Fuzzy Systems, Data Mining, Signal Processing, Image processing, Text Processing, Wireless Sensor Networks, Network Security and Cellular Automata.
Download or read book Graphics Recognition Ten Years Review and Future Perspectives written by Wenyin Liu and published by Springer. This book was released on 2006-10-15 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 6th International Workshop on Graphics Recognition, GREC 2005, held in Hong Kong, China, August 2005. The book presents 37 revised full papers together with a panel discussion report, organized in topical sections on engineering drawings vectorization and recognition, symbol recognition, graphic image analysis, structural document analysis, sketching and online graphics recognition, curves and shape processing, and graphics recognition contest results.
Download or read book Smart Computing and Informatics written by Suresh Chandra Satapathy and published by Springer. This book was released on 2017-10-28 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains 68 papers presented at SCI 2016: First International Conference on Smart Computing and Informatics. The conference was held during 3-4 March 2017, Visakhapatnam, India and organized communally by ANITS, Visakhapatnam and supported technically by CSI Division V – Education and Research and PRF, Vizag. This volume contains papers mainly focused on smart computing for cloud storage, data mining and software analysis, and image processing.
