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 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 Pattern Mining with Evolutionary Algorithms written by Sebastián Ventura and published by Springer. This book was released on 2016-06-13 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness.

Download or read book Data Mining Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2012-11-30 with total page 2335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.

Download or read book Research Methods Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2015-01-31 with total page 2107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Across a variety of disciplines, data and statistics form the backbone of knowledge. To ensure the reliability and validity of data, appropriate measures must be taken in conducting studies and reporting findings. Research Methods: Concepts, Methodologies, Tools, and Applications compiles chapters on key considerations in the management, development, and distribution of data. With its focus on both fundamental concepts and advanced topics, this multi-volume reference work will be a valuable addition to researchers, scholars, and students of science, mathematics, and engineering.

Download or read book Frequent Pattern Discovery from Gene Expression Data written by Shruti Mishra and published by LAP Lambert Academic Publishing. This book was released on 2012-04 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is usually mentioned in the broader setting of knowledge discovery in databases (KDD), and is viewed as a single step in a larger process called the KDD process. Frequent Pattern Mining (FPM) plays a vital role especially in the real time data mining research because of its wide applicability in industry areas, including process control, production data mining and many other important real time data mining tasks. Creating an association between variables is always of interest in genomic studies. FPM has been applied successfully for discovering interesting association patterns between various genes. Motivated by several heuristics to reduce the number of database scans in the context of frequent pattern mining, the concept of fuzziness on the original gene expression data set was provided in order to discretize the value in terms of under expressed and over expressed genes. Certain soft computing approaches were used to optimize the findings and generate frequent patterns based on the fuzzy frequent pattern mining algorithms. It was observed that fuzzy set helped a lot to find better results in terms of number of frequent patterns.

Download or read book Big Data Analytics written by Mrutyunjaya Panda and published by CRC Press. This book was released on 2018-12-12 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

Download or read book Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications written by Bhattacharyya, Siddhartha and published by IGI Global. This book was released on 2015-11-03 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conventional computational methods, and even the latest soft computing paradigms, often fall short in their ability to offer solutions to many real-world problems due to uncertainty, imprecision, and circumstantial data. Hybrid intelligent computing is a paradigm that addresses these issues to a considerable extent. The Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications highlights the latest research on various issues relating to the hybridization of artificial intelligence, practical applications, and best methods for implementation. Focusing on key interdisciplinary computational intelligence research dealing with soft computing techniques, pattern mining, data analysis, and computer vision, this book is relevant to the research needs of academics, IT specialists, and graduate-level students.

Download or read book Landscape of Next Generation Sequencing Using Pattern Recognition written by Saurav Mallik and published by CRC Press. This book was released on 2024-10-23 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on an eminent technology called next generation sequencing (NGS) which has entirely changed the procedure of examining organisms and will have a great impact on biomedical research and disease diagnosis. Numerous computational challenges have been brought on by the rapid advancement of large-scale next-generation sequencing (NGS) technologies and their application. The term ""biomedical imaging"" refers to the use of a variety of imaging techniques (such as X-rays, CT scans, MRIs, ultrasounds, etc.) to get images of the interior organs of a human being for potential diagnostic, treatment planning, follow-up, and surgical purposes. In these circumstances, deep learning, a new learning method that uses multi-layered artificial neural networks (ANNs) for unsupervised, supervised, and semi-supervised learning, has attracted a lot of interest for applications to NGS and imaging, even when both of these data are used for the same group of patients. The three main research phenomena in biomedical research are disease classification, feature dimension reduction, and heterogeneity. AI approaches are used by clinical researchers to efficiently analyse extremely complicated biomedical datasets (e.g., multi-omic datasets. With the use of NGS data and biomedical imaging of various human organs, researchers may predict diseases using a variety of deep learning models. Unparalleled prospects to improve the work of radiologists, clinicians, and biomedical researchers, speed up disease detection and diagnosis, reduce treatment costs, and improve public health are presented by using deep learning models in disease prediction using NGS and biomedical imaging. This book influences a variety of critical disease data and medical images.

Download or read book Optimal and Robust Rule Set Generation written by Jiuyong Li and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The rapidly growing volume and complexity of modern databases makes the need for technologies to describe and summarise the information they contain increasingly important. Data mining is a process of extracting implicit, previously unknown and potentially useful patterns and relationships from data, and is widely used in industry and business applications. -- Rules characterise relationships among patterns in databases, and rule mining is one of the central tasks in data mining. There are fundamentally two categories of rules, namely association rules and classification rules. Traditionally, association rules are connected with transaction databases for market basket problems and classification rules are associated with relational databases for predictions. In this thesis, we will mainly focus on the use of association rules for predictions. -- An optimal rule set is a rule set that satisfies given optimality criteria. In this thesis we study two types of optimal rule sets, the informative association rule set and the optimal class association rule set, where the informative association rule set is used for market basket predictions and the class association rule set is used for the classification. A robust classification rule set is a rule set that is capable of providing more correct predictions than a traditional classification rule set on incomplete test data. -- Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. We define a rule set for a given transaction database that is significantly smaller than an association rule set but makes the same predictions as the complete association rule set. We call this rule set the informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We characterise the relationships between the informative rule set and the non-redundant association rule set. We present an algorithm to directly generate the informative rule set without generating all frequent itemsets first, and that accesses databases less often than other direct methods. We show experimentally that the informative rule set is much smaller than both the association rule set and the non-redundant association rule set for a given database, and that it can be generated more efficiently. In addition, we discuss a new unsupervised discretization method to deal with numerical attributes in general association rule mining without target specification. Based on the analysis of the strengths and weaknesses of two commonly used unsupervised numerical attribute discretization methods, we present an adaptive numerical attribute merging algorithm that is shown better than both methods in general association rule mining. -- Relational databases are usually denser than transaction databases, so mining on them for class association rules, which is a set of association rules whose consequences are classes, may be difficult due to the combinatorial explosion. Based on the analysis of the prediction mechanism, we define an optimal class association rule set to be a subset of the complete class association rule set containing all potentially predictive rules. -- Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining optimal class association rule sets using upward closure properties to prune weak rules before they are actually generated. We show theoretically the efficiency of the proposed algorithm will be greater than Apriori on dense databases, and confirm experimentally that it generates an optimal class association rule set, which is very much smaller than a complete class association rule set, in significantly less time than generating the complete class association rule set by Apriori. -- Traditional classification rule sets perform badly on test data that are not as complete as the training data. We study the problem of discovering more robust rule sets, i.e. we say a rule is more robust than another rule set if it is able to make more accurate predictions on test data with missing attribute values. We reveal a hierarchy of k-optimal rule sets where a k-optimal rule set with a large k is more robust, and they are more robust than a traditional classification rule set. We introduce two methods to find k-optimal rule sets, i.e. an optimal association rule mining approach and a heuristic approximate approach. We show experimentally that a k-optimal rule set generated from the optimal association rule mining approach performs better than that from the heuristic approximate approach and both rule sets perform significantly better than a typical classification rule set (C4.5Rules) on incomplete test data. -- Finally, we summarise the work discussed in this thesis, and suggest some future research directions.

Download or read book E Learning Systems written by Aleksandra Klašnja-Milićević and published by Springer. This book was released on 2016-07-19 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques.

Download or read book Mining Social Networks and Security Informatics written by Tansel Özyer and published by Springer Science & Business Media. This book was released on 2013-06-01 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crime, terrorism and security are in the forefront of current societal concerns. This edited volume presents research based on social network techniques showing how data from crime and terror networks can be analyzed and how information can be extracted. The topics covered include crime data mining and visualization; organized crime detection; crime network visualization; computational criminology; aspects of terror network analyses and threat prediction including cyberterrorism and the related area of dark web; privacy issues in social networks; security informatics; graph algorithms for social networks; general aspects of social networks such as pattern and anomaly detection; community discovery; link analysis and spatio-temporal network mining. These topics will be of interest to researchers and practitioners in the general area of security informatics. The volume will also serve as a general reference for readers that would want to become familiar with current research in the fast growing field of cybersecurity.

Download or read book Inductive Databases and Constraint Based Data Mining written by Sašo Džeroski and published by Springer Science & Business Media. This book was released on 2010-11-18 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.

Download or read book Association Rule Mining written by Chengqi Zhang and published by Springer. This book was released on 2003-08-01 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

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 Advances in Knowledge Discovery and Data Mining written by De-Nian Yang and published by Springer Nature. This book was released on with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Progress in Intelligent Computing Techniques Theory Practice and Applications written by Pankaj Kumar Sa and published by Springer. This book was released on 2017-07-12 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on both theory and applications in the broad areas of communication technology, computer science and information security. This two volume book contains the Proceedings of 4th International Conference on Advanced Computing, Networking and Informatics. This book brings together academic scientists, professors, research scholars and students to share and disseminate information on knowledge and scientific research works related to computing, networking, and informatics to discuss the practical challenges encountered and the solutions adopted. The book also promotes translation of basic research into applied investigation and convert applied investigation into practice.