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Mining Sequential Patterns From Large Data Sets
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Book Synopsis Mining Sequential Patterns from Large Data Sets by : Wei Wang
Download or read book Mining Sequential Patterns from Large Data Sets written by Wei Wang and published by Springer Science & Business Media. This book was released on 2005-07-26 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.
Book Synopsis Proceedings of the Third SIAM International Conference on Data Mining by : Daniel Barbara
Download or read book Proceedings of the Third SIAM International Conference on Data Mining written by Daniel Barbara and published by SIAM. This book was released on 2003-01-01 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 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.
Book Synopsis Mining Sequential Patterns from Large Data Sets by : Wei Wang
Download or read book Mining Sequential Patterns from Large Data Sets written by Wei Wang and published by Springer Science & Business Media. This book was released on 2005-02-28 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.
Book Synopsis Sequence Data Mining by : Guozhu Dong
Download or read book Sequence Data Mining written by Guozhu Dong and published by Springer Science & Business Media. This book was released on 2007-10-31 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.
Book Synopsis Frequent Pattern Mining by : Charu C. Aggarwal
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.
Book Synopsis Data Mining for Association Rules and Sequential Patterns by : Jean-Marc Adamo
Download or read book Data Mining for Association Rules and Sequential Patterns written by Jean-Marc Adamo and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in data collection, storage technologies, and computing power have made it possible for companies, government agencies and scientific laboratories to keep and manipulate vast amounts of data relating to their activities. This state-of-the-art monograph discusses essential algorithms for sophisticated data mining methods used with large-scale databases, focusing on two key topics: association rules and sequential pattern discovery. This will be an essential book for practitioners and professionals in computer science and computer engineering.
Book Synopsis Periodic Pattern Mining by : R. Uday Kiran
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.
Book Synopsis High-Utility Pattern Mining by : Philippe Fournier-Viger
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 337 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.
Book Synopsis Mining of Massive Datasets by : Jure Leskovec
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.
Book Synopsis Mining High Utility Patterns Over Data Streams by : Morteza Zihayat Kermani
Download or read book Mining High Utility Patterns Over Data Streams written by Morteza Zihayat Kermani and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mining useful patterns from sequential data is a challenging topic in data mining. An important task for mining sequential data is sequential pattern mining, which discovers sequences of itemsets that frequently appear in a sequence database. In sequential pattern mining, the selection of sequences is generally based on the frequency/support framework. However, most of the patterns returned by sequential pattern mining may not be informative enough to business people and are not particularly related to a business objective. In view of this, high utility sequential pattern (HUSP) mining has emerged as a novel research topic in data mining recently. The main objective of HUSP mining is to extract valuable and useful sequential patterns from data by considering the utility of a pattern that captures a business objective (e.g., profit, users interest). In HUSP mining, the goal is to find sequences whose utility in the database is no less than a user-specified minimum utility threshold. Nowadays, many applications generate a huge volume of data in the form of data streams. A number of studies have been conducted on mining HUSPs, but they are mainly intended for non-streaming data and thus do not take data stream characteristics into consideration. Mining HUSP from such data poses many challenges. First, it is infeasible to keep all streaming data in the memory due to the high volume of data accumulated over time. Second, mining algorithms need to process the arriving data in real time with one scan of data. Third, depending on the minimum utility threshold value, the number of patterns returned by a HUSP mining algorithm can be large and overwhelms the user. In general, it is hard for the user to determine the value for the threshold. Thus, algorithms that can find the most valuable patterns (i.e., top-k high utility patterns) are more desirable. Mining the most valuable patterns is interesting in both static data and data streams. To address these research limitations and challenges, this dissertation proposes techniques and algorithms for mining high utility sequential patterns over data streams. We work on mining HUSPs over both a long portion of a data stream and a short period of time. We also work on how to efficiently identify the most significant high utility patterns (namely, the top-k high utility patterns) over data streams. In the first part, we explore a fundamental problem that is how the limited memory space can be well utilized to produce high quality HUSPs over the entire data stream. An approximation algorithm, called MAHUSP, is designed which employs memory adaptive mechanisms to use a bounded portion of memory, to efficiently discover HUSPs over the entire data streams. The second part of the dissertation presents a new sliding window-based algorithm to discover recent high utility sequential patterns over data streams. A novel data structure named HUSP-Tree is proposed to maintain the essential information for mining recenT HUSPs. An efficient and single-pass algorithm named HUSP-Stream is proposed to generate recent HUSPs from HUSP-Tree. The third part addresses the problem of top-k high utility pattern mining over data streams. Two novel methods, named T-HUDS and T-HUSP, for finding top-k high utility patterns over a data stream are proposed. T-HUDS discovers top-k high utility itemsets and T-HUSP discovers top-k high utility sequential patterns over a data stream. T-HUDS is based on a compressed tree structure, called HUDS-Tree, that can be used to efficiently find potential top-k high utility itemsets over data streams. T-HUSP incrementally maintains the content of top-k HUSPs in a data stream in a summary data structure, named TKList, and discovers top-k HUSPs efficiently. All of the algorithms are evaluated using both synthetic and real datasets. The performances, including the running time, memory consumption, precision, recall and Fmeasure, are compared. In order to show the effectiveness and efficiency of the proposed methods in reallife applications, the fourth part of this dissertation presents applications of one of the proposed methods (i.e., MAHUSP) to extract meaningful patterns from a real web clickstream dataset and a real biosequence dataset. The utility-based sequential patterns are compared with the patterns in the frequency/support framework. The results show that high utility sequential pattern mining provides meaningful patterns in real-life applications.
Book Synopsis Pattern Discovery Using Sequence Data Mining by : Pradeep Kumar
Download or read book Pattern Discovery Using Sequence Data Mining written by Pradeep Kumar and published by . This book was released on 2011-07-01 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"--
Book Synopsis Searching for Frequent Sequential Patterns in Large Datasets by : Linhui Jiang
Download or read book Searching for Frequent Sequential Patterns in Large Datasets written by Linhui Jiang and published by . This book was released on 2003 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Database Technology EDBT '96 by : Peter Apers
Download or read book Advances in Database Technology EDBT '96 written by Peter Apers and published by Springer. This book was released on 1996-03-18 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the Fifth International Conference on Extending Database Technology, EDBT'96, held in Avignon, France in March 1996. The 31 full revised papers included were selected from a total of 178 submissions; also included are some industrial-track papers, contributed by partners of several ESPRIT projects. The volume is organized in topical sections on data mining, active databases, design tools, advanced DBMS, optimization, warehousing, system issues, temporal databases, the web and hypermedia, performance, workflow management, database design, and parallel databases.
Book Synopsis Sequential Pattern Generalization for Mining Multi-source Data by : Julie Bu Daher
Download or read book Sequential Pattern Generalization for Mining Multi-source Data written by Julie Bu Daher and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Huge amounts of digital data have been created across years due to the increasing digitization in our everyday life. As a consequence, fast data collection and storage tools have been developed and data can be collected in huge volumes for various research and business purposes. The collected data can come from multiple data sources and can be of heterogeneous kinds thus forming heterogeneous multi-source datasets, and they can be analyzed to extract valuable information. Data mining is an important task in discovering interesting information from datasets. Different approaches in this domain have been proposed, among which pattern mining is the most important one. Pattern mining, including sequential pattern mining, discovers statistically relevant patterns (or sequential patterns) among data. The challenges of this domain include discovering important patterns with a limited complexity and by avoiding redundancy among the resulting patterns. Multi-source data could represent descriptive and sequential data, making the mining process complex. There could be problems of data similarity on one source level which leads to a limited number of extracted patterns. The aim of the thesis is to mine multi-source data to obtain valuable information and compensate the loss of patterns due to the problem of similarity with a limited complexity and by avoiding pattern redundancy. Many approaches have been proposed to mine multi-source data. These approaches either integrate multi-source data and perform a single mining process which increases the complexity and generates a redundant set of sequential patterns, or they mine sources separately and integrate the results which could lead to a loss of patterns. We propose G_SPM, a general sequential pattern mining algorithm that takes advantage of multi-source data to mine general patterns which compensates the loss of patterns caused by the problem of data similarity. These rich patterns contain various kinds of information and have higher data coverage than traditional patterns. G_SPM adopts a selective mining strategy of data sources where a main source is first mined, and other sources are mined only when similarity among patterns is detected, which limits the complexity and avoids pattern redundancy. The experimental results confirm that G_SPM succeeds in mining general patterns with a limited complexity. In addition, it outperforms traditional approaches in terms of runtime and pattern redundancy.
Book Synopsis R: Mining spatial, text, web, and social media data by : Bater Makhabel
Download or read book R: Mining spatial, text, web, and social media data written by Bater Makhabel and published by Packt Publishing Ltd. This book was released on 2017-06-19 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.
Book Synopsis Mining Big Data for Frequent Patterns Using MapReduce Computing by : Sumalatha Saleti
Download or read book Mining Big Data for Frequent Patterns Using MapReduce Computing written by Sumalatha Saleti and published by . This book was released on 2023-08-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main motivation of frequent pattern mining is to extract useful patterns from the data sets. Interesting associations among the data can be discovered by mining the frequent patterns. Among the different kinds of pattern mining, frequent itemset mining has been applied widely in many applications such as market basket analysis, medical applications, online transactions, social network analysis and so forth. An itemset is called frequent if the set of items in it appear frequently together. However, frequent itemset mining can find only the frequent itemsets, the time regularity of the items cannot be found. Sequential pattern mining considers both the frequency of the items and the order of items based on their time stamps. It attracted great deal of attention in many applications such as customer buying trend analysis, web access mining, natural disaster analysis and so forth. The patterns mined from sequential pattern mining algorithms do not consider the cost or profit of the item. A sequence that is not frequent in a dataset may contribute much to the overall profit of the organization due to its high profit. Hence, utility sequential pattern mining considers quantity and timestamp of items as well as profit of each item. Because of constantly arriving new data, the resultant patterns of frequent pattern mining may become obsolete over time. Hence, it is necessary to incrementally process the data in order to refresh the mining results without mining from scratch. The advancement in technology led to the generation of huge volumes of data from multiple sources such as social media, online transactions, internet applications and so forth. This era of big data pose a challenge to explore large volumes of data and extract the knowledge in the form of useful patterns. Moreover, the conventional methods used in mining patterns are not suitable for handling the big data. Hence, in this thesis, we investigate the solutions for frequent pattern mining on big data using a popular programming model known as MapReduce. Firstly, we propose a parallel algorithm for compressing the transactional data that makes the data simple and Bit Vector Product algorithm is proposed to mine the frequent itemsets from the compressed data. Secondly, distributed algorithm for mining sequential patterns using cooccurrence information is proposed. Here, we make use of item co-occurrence information and reduce the search space using the pruning strategies. Thirdly, distributed high utility time interval sequential patterns with time information between the successive items are mined. Finally, an incremental algorithm is proposed to make use of the knowledge obtained in ii previous mining while mining sequential patterns. All the proposed algorithms are tested on our in house Hadoop cluster composed of one master node and eight data nodes.
Book Synopsis Data Preparation for Data Mining by : Dorian Pyle
Download or read book Data Preparation for Data Mining written by Dorian Pyle and published by Morgan Kaufmann. This book was released on 1999-03-22 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.
