More and more information about business processes is recorded by information systems in the form of so-called event logs. Despite the omnipresence of such data, most organizations diagnose problems based on fiction rather than facts. Process mining is an emerging discipline based on process model-driven approaches and data mining. It not only allows organizations to fully benefit from the information stored in their systems, but it can also be used to check the conformance of processes, detect bottlenecks, and predict execution problems.Wil van der Aalst delivers the first book on process mining. It aims to be self-contained while covering the entire process mining spectrum from process discovery to operational support. In Part I, the author provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Part II focuses on process discovery as the most important process mining task. Part III moves beyond discovering the control flow of processes and highlights conformance checking, and organizational and time perspectives. Part IV guides the reader in successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM. Finally, Part V takes a step back, reflecting on the material presented and the key open challenges.Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
PThis book constitutes the thoroughly refereed post-proceedings of the 4th Industrial Conference on Data Mining, ICDM 2004, held in Leipzig, Germany on July 2004./P PThe conference was focused on advanced data mining applications in image mining, medicine and bioinformatics, management and environmental control, and telecommunications. The 18 revised full papers presented were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections on case-based reasoning, image mining, applications in process control and insurance, clustering and association rules, telecomunications, and medicine and biotechnology./P
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.
Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby advocating a series of papers at the intersection of data mining and information systems research. This special issue of Annals of Information Systems contains original papers and substantial extensions of selected papers from the 2007 and 2008 International Conference on Data Mining (DMIN07 and DMIN08, Las Vegas, NV) that have been rigorously peer-reviewed. The issue brings together topics on both information systems and data mining, and aims to give the reader a current snapshot of the contemporary research and state of the art practice in data mining. Among the suggested topics of interest were: Predictive data mining; managerial decision support; data mining applications in marketing, operations management, finance, logistics and supply chain management; data warehousing and business intelligence; document classification and web-usage mining; association rule mining and market basket analysis; security, privacy and social impact of data mining
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Placer mining is the mining of alluvial deposits for minerals. This may be done by open-pit (also called open-cast mining) or by various forms of tunneling into ancient riverbeds. Excavation may be accomplished using water pressure (hydraulic mining), surface excavating equipment or tunneling equipment. The name derives from Spanish, placer, meaning ´´sandbank.´´ It refers to mining the precious metal deposits (particularly gold and gemstones) found in alluvial deposits-deposits of sand and gravel in modern or ancient stream beds. The metal or gemstones, having been moved by stream flow from an original source such as a vein, is typically only a minuscule portion of the total deposit. The containing material may be too loose to safely mine by tunneling. Where water under pressure is available, it may be used to mine, move, and separate the precious material from the deposit.
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.
WWW continues to grow at an overwhelming rate in both the sheer volume of traffic and the size & complexity of Web sites. Therefore, it becomes more and more necessary, but difficult to get useful information from Web data, in order to understand and better serve the needs of Web-based applications. As a result, the Web usage mining has become a hot research topic, which combines two of the prominent research areas comprising the data mining and the World Wide Web.The book gives all the details of two contributions from this research work those are preprocessing of web log data and clustering of web users based on thier interest. ART1 NN algorithm is used for clustering and finally it has been proved that ART1 performs better than traditional k-means and SOM clustering algorithms.
This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student´s academic success; 5) detect student´s personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.
Yan Zhao´s Ph.D. dissertation, entitled ´´Interactive Data Mining´´ provides a conceptual framework and a systematic study of human-computer interactions and collaborations for effective data mining. The thesis is based on an assumption that the effectiveness of data mining systems depends crucially on semantics information about data and different user requirements. In contrast to many data mining models that concentrate on automation and efficiency, interactive data mining systems focus on adaptive and effective communication between human users and computer systems. Interactive systems fully explore the power of human intuition, creativity, heuristics and strategies with supports from computers. The thesis is well-balanced between theoretical investigation and experimental evaluations. A user-oriented three-layered conceptual model is proposed. Within the framework, user perceptions and requirements are studied formally at the philosophical, technique and the application layers. The separation of the three layers leads to many new insights into data mining. Based on the conceptual framework, a prototype of Interactive Classification System (ICS) has been implemented.