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 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 Advances in Knowledge Discovery and Data Mining written by Honghua Dai and published by Springer Science & Business Media. This book was released on 2004-05-11 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, held in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining.

Download or read book Advances in Knowledge Discovery and Data Mining written by Thanaruk Theeramunkong and published by Springer. This book was released on 2009-04-21 with total page 1098 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009. The 39 revised full papers and 73 revised short papers presented together with 3 keynote talks were carefully reviewed and selected from 338 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.

Download or read book High Utility Pattern Mining written by Philippe Fournier-Viger and published by Springer. This book was released on 2019-01-18 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data. The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.

Download or read book Frequent Pattern Mining in Transactional and Structured Databases written by Renáta Iváncsy and published by LAP Lambert Academic Publishing. This book was released on 2010-10-01 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is a process of discovering hidden relationships in large amounts of data. Frequent pattern discovery is an important research area in the field of data mining. Its purpose is to find patterns which appear frequently in a large collection of data. This work deals with three main areas of frequent pattern mining, namely, frequent itemset, frequent sequence and frequent subtree discovery. Beside providing a brief overview of related works of each single frequent pattern mining problem mentioned before, the three theses offered in this work suggest novel methods for efficient discovery of the different types of frequent patterns. The new methods are compared to the best-known algorithms in the related fields. The performance analysis of the methods involves measurements of the execution time and memory requirements.

Download or read book Periodic Pattern Mining written by R. Uday Kiran and published by Springer Nature. This book was released on 2021-10-29 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.

Download or read book A Genetic Based Research Framework to Discover Optimal Frequent Patterns Using Association Rule Mining written by Prof. V. V. R. Maheswara Rao and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid advances in data generation, availability of automated tools in data collection and continued decline in data storage cost enabled with high volumes of data. In addition, the datais non scalable, high dimensional, heterogeneous and complex in its nature. This situation creates inevitably increasing challenges in extracting desired information. Thus, Data mining evolves into a fertile area and got the focus by many researchers and business analysts. Data mining is a methodology the blends traditional techniques with sophisticated algorithms. Among all, the association rule mining is efficient pattern discovery technique, which finds hidden, valid, novel, useful, understandable, interesting and ultimately correlated patterns in large databases.Such correlated rules create great business value to any organization as they make use in decision making process. However, in real time applications the correlation changes continuously as the source data updates dynamically. This motivation necessitates finding and updating the frequent item sets with different supports efficiently and optimally. In order to overcome the challenges inherited in conventional association rule mining, the authors in the present paper propose an Optimal Frequent Patterns System (OFPS). The OFPS takes radically a different approach and design as a three-fold system that discovers optimal frequent patterns efficiently, using the genetic algorithm. Initially, the first-fold of OFPS focuses on preparation of domain specific data that includes data selection, cleaning, integration and transformation under the guidance of knowledge expert. Subsequently, the second-fold of OFPS emphasizes on construction of a Frequent Pattern Tree (FP-Tree) and then discovery of frequent patterns by exploring the tree in the bottom-up fashion to facilitate rapid access of individual frequent patterns quickly. The third-fold of OFPS finally concentrates on generation of optimal frequent patterns using genetic algorithm that simulates biological evaluation procedure having the self learning capability. To validate the performance of proposed OFPS in several orders of magnitude, many experiments were conducted and results have proven this as claimed.

Download or read book Complex Pattern Mining written by Annalisa Appice and published by Springer Nature. This book was released on 2020-01-14 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the challenges facing current research in knowledge discovery and data mining posed by the huge volumes of complex data now gathered in various real-world applications (e.g., business process monitoring, cybersecurity, medicine, language processing, and remote sensing). The book consists of 14 chapters covering the latest research by the authors and the research centers they represent. It illustrates techniques and algorithms that have recently been developed to preserve the richness of the data and allow us to efficiently and effectively identify the complex information it contains. Presenting the latest developments in complex pattern mining, this book is a valuable reference resource for data science researchers and professionals in academia and industry.

Download or read book Data Mining Patterns New Methods and Applications written by Poncelet, Pascal and published by IGI Global. This book was released on 2007-08-31 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an overall view of recent solutions for mining, and explores new patterns,offering theoretical frameworks and presenting challenges and possible solutions concerning pattern extractions, emphasizing research techniques and real-world applications. It portrays research applications in data models, methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming and incremental mining"--Provided by publisher.

Download or read book Emerging Technologies in Knowledge Discovery and Data Mining written by Takashi Washio and published by Springer Science & Business Media. This book was released on 2007-12-14 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of three workshops and an industrial track held in conjunction with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China in May 2007. The 62 revised full papers presented together with an overview article to each workshop were carefully reviewed and selected from 355 submissions.

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 Algorithms for Multi level Frequent Pattern Minning written by Zheng, Xi and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Mining Concepts and Techniques written by Jiawei Han and published by Elsevier. This book was released on 2011-06-09 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - 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 - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Download or read book Data Warehousing and Knowledge Discovery written by A Min Tjoa and published by Springer Science & Business Media. This book was released on 2005-08-17 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2005, held in Copenhagen, Denmark, in August 2005. The 51 revised full papers presented were carefully reviewed and selected from 196 submissions. The papers are organized in topical sections on data warehouses, evaluation and tools, schema transformations, materialized views, aggregates, data warehouse queries and database processing issues, data mining algorithms and techniques, association rules, text processing and classification, security and privacy issues, patterns, and cluster and classification.

Download or read book Exploring Advances in Interdisciplinary Data Mining and Analytics New Trends written by Taniar, David and published by IGI Global. This book was released on 2011-12-31 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is an updated look at the state of technology in the field of data mining and analytics offering the latest technological, analytical, ethical, and commercial perspectives on topics in data mining"--Provided by publisher.

Download or read book International Conference on Computer Applications Database Systems written by and published by Research Publishing Service. This book was released on with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: