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Advances in Knowledge Discovery in Databases by Animesh Adhikari,Jhimli Adhikari Pdf
This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.
Advances in Knowledge Discovery and Data Mining by Usama M. Fayyad Pdf
Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Advances in Knowledge Discovery and Data Mining by Qiang Yang,Zhi-Hua Zhou,Zhiguo Gong,Min-Ling Zhang,Sheng-Jun Huang Pdf
The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 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, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.
Advances in Knowledge Discovery and Management by Fabrice Guillet,Gilbert Ritschard,Djamel A. Zighed Pdf
During the last decade, the French-speaking scientific community developed a very strong research activity in the field of Knowledge Discovery and Management (KDM or EGC for “Extraction et Gestion des Connaissances” in French), which is concerned with, among others, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and SemanticWeb. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2009 Conference held in Strasbourg, France on January 2009. The volume is organized in four parts. Part I includes five papers concerned by various aspects of supervised learning or information retrieval. Part II presents five papers concerned with unsupervised learning issues. Part III includes two papers on data streaming and two on security while in Part IV the last four papers are concerned with ontologies and semantic.
Advances in Knowledge Discovery and Data Mining, Part I by Pang-Ning Tan,Sanjay Chawla,Chin Kuan Ho,James Bailey Pdf
The two-volume set LNAI 7301 and 7302 constitutes the refereed proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in May 2012. The total of 20 revised full papers and 66 revised short papers were carefully reviewed and selected from 241 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas. The papers are organized in topical sections on supervised learning: active, ensemble, rare-class and online; unsupervised learning: clustering, probabilistic modeling in the first volume and on pattern mining: networks, graphs, time-series and outlier detection, and data manipulation: pre-processing and dimension reduction in the second volume.
This book presents six thoroughly reviewed and revised full papers describing selected projects on data mining. Three papers deal with data mining and e-commerce, focusing on sequence rule analysis, association rule mining and knowledge discovery in databases, and intelligent e-marketing with Web mining. One paper is devoted to experience management and process learning. The last two papers report on medical applications, namely on genomic data processing and on case-based reasoning for prognosis of influenza.
Knowledge Discovery in Multiple Databases by Shichao Zhang,Chengqi Zhang,Xindong Wu Pdf
The Web has emerged as a large, distributed data repository, and information on the Internet and in existing transaction databases can be analyzed for commercial gains in decision making. Therefore, how to efficiently identify quality knowledge from different data sources uncovers a significant challenge. This challenge has attracted wide interest from both academia and the industry. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, and presents a local-pattern analysis framework for pattern discovery from multiple data sources. Based on this framework, data preparation techniques in multiple databases, an application-independent database classification for data reduction, and efficient algorithms for pattern discovery from multiple databases are described. Knowledge Discovery in Multiple Databases is suitable for researchers, professionals and students in data mining, distributed data analysis, and machine learning, who are interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might involve knowledge discovery in databases and data mining.
Advances in Knowledge Discovery and Data Mining by Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho Pdf
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.
Data Mining and Knowledge Discovery for Process Monitoring and Control by Xue Z. Wang Pdf
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
Advanced Methods for Knowledge Discovery from Complex Data by Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook Pdf
The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.
Advances in Knowledge Discovery and Data Mining by Kamal Karlapalem,Hong Cheng,Naren Ramakrishnan,R. K. Agrawal,P. Krishna Reddy,Jaideep Srivastava,Tanmoy Chakraborty Pdf
The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.
Advances in Knowledge Discovery and Data Mining by Jian Pei,Vincent S. Tseng,Longbing Cao,Hiroshi Motoda,Guandong Xu Pdf
The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, applications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving data mining, recommendation, multimedia data mining, stream data mining, data preprocessing and representation.
Advances in Knowledge Discovery and Data Mining by Joshua Zhexue Huang,Longbing Cao,Jaideep Srivastava Pdf
The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 revised full papers and 58 revised short papers were carefully reviewed and selected from 331 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, machine learning, artificial intelligence and pattern recognition, data warehousing and databases, statistics, knoweldge engineering, behavior sciences, visualization, and emerging areas such as social network analysis.
Advances in Knowledge Discovery and Data Mining by Hady W. Lauw,Raymond Chi-Wing Wong,Alexandros Ntoulas,Ee-Peng Lim,See-Kiong Ng,Sinno Jialin Pan Pdf
The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 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, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.