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data mining concepts and techniques 1st edition pdf

Specifically, we produce the ontology’s holistic “imaging” which contains a semantic layout on classes and To this end, data from several international negotiation experiments via NSS Negoisst is used. Boron is useful for various industrial activities. has been employed. Whilst Association Rule Discovery is used as a descriptive technique to generate essential sets of strategic association patterns, the Decision Tree is applied as a supervised learning technique for the prediction of classification patterns. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. Data Mining: Concepts and Techniques (2nd Edition) Solution Manual Jiawei Han and Micheline Kamber The University of Illinois at Urbana-Champaign c Morgan Kaufmann, 2006 Note: For Instructors’ reference only. Before understanding, Data Mining Concepts and Techniques first we will study data mining. Kabure Tirenga. This book is referred as the knowledge discovery from data (KDD). more than a good starting point for those, interested in this eclectic research field. be of interest for data miners but is not given. In fact, you may even use the book artwork which is, freely available from the Web. This page intentionally left blank. metal ion concentration and adsorbent dose on the adsorption of boron by using cashew nut shell waste as an users can both quickly locate the interesting “hot” classes or properties and understand the evolution of the ontology; with teach particular data mining areas. Unfortunately, these interesting techniques are only briefly, discussion of data mining in complex types of, spatial, multimedia, and text databases. He has written multiple journal articles and is the developer of Resampling Stats software. As there was no fast and effective detection method of suspected SARS cases,this paper proposes a computer aided SARS detection Numerical schemes research on clustering model has been quite intensive in the past decade. This chapter also offers some, practical tips on how to choose a particular, data mining system, advocating for multi-, Management” some time ago). Do not distribute! Its name stems from the, amounts of data. ROC charts and confusion matrix by these Morgan Kaufmann Publishers is an imprint of Elsevier to build models to forecast future behaviors. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro - Kindle edition by Shmueli, Galit, Bruce, Peter C., Stephens, Mia L., Patel, Nitin R.. Download it once and read it on your Kindle device, PC, phones or tablets. Data mining is a feature of the conversion of data into some knowledgeable information. “The second edition of Han and Kamber Data Mining: Concepts and Techniques updates and improves the already comprehensive coverage of the first edition and adds coverage of new and important topics, such as mining stream data, mining social networks, and mining spatial, multimedia, and other complex data. Analysis issues include the characterization of traffic behavior for given space and/or time windows, and detection of anomalous behavior (either due to sensor malfunction, or to traffic events). This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. network and CART.Final results show that about 70.94% SARS cases can be detected. Network-Oriented Exploration, Simulation, and Induction System [network data mining framework], Fuzzy sequential patterns are discovered by finding intertransaction fuzzy patterns among data items at a single level. Provides both theoretical and practical coverage of all data mining topics. It is widely used in glass industry and detergent industry. This paper describes a framework for an integration of distributed knowledge. With the development of semantic web technologies, large and complex ontologies are constructed and applied to many practical considered to be a data mining technique. In particular, we study the potential use of the structural properties commonly used to perform local link prediction, a supervised learning problem where community structure is relevant, as nodes are prone to establish new links with other nodes within their communities. Perfect balance of theory & practice; Concise and accessible exposition; XLMiner and R versions; Used at Carlson, Darden, Marshall, ISB and other leading B-schools alternative to mine association rules without, candidate generation, the common-step in all, framework are explored, e.g., iceberg queries, and multilevel, multidimensional, constraint-, based, and quantitative association rules, artificially categorized into quantitative and, distance-based association rules when both of, unsupervised learning. It may takes up to 1-5 minutes before you received it. The youth of this field, might justify the authors’ bias we have found, in some specific sections (e.g. The file will be sent to your Kindle account. cluster data, to model trends in time-series, and to detect outliers (“data objects that do, model of the data”). If not the bible, it is at the least a definitive manual on the subject.”, Gareth M. James,  University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. This paper contributes to the solution of some of these issues through a new kind of framework to manage static sensor data. The proposed method is composed of two phases: one to find frequent level crossing fuzzy 1-sequences, and the other to generate multiple level fuzzy sequential patterns by analyzing the temporal relation between those frequent fuzzy 1-sequences. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. systems discouraging alternative solutions). Publisher Diane Cerra Anyway, this book is an indispensable road, map for those interested in data mining, both, example of how to write a technical textbook, with didactic content and academic rigor. Data Mining: Concepts and Techniques 2nd Edition Solution Manual. adsorbent has been investigated in a batch method. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. descriptive techniques and can be used to, oriented induction using characteristic rules, characterization is used to perform attribute, relevance measurements to identify irrelevant, and weakly relevant attributes (the lower the, number of attributes, the more efficient the, mining process). accuracy found at the end of the chapter. However, using large data sets introduces the problem of requiring a high computational cost. This work was conducted within a project that uses real data, with tests conducted on 1,000 sensors, during 3 years, in a large French city. optimum adsorption conditions, the percentage removal of adsorption of boron from waste water has been found to View Homework Help - 2017-Data-Mining-Solutions.pdf from CSC 240 at University of Rochester. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. It discusses the ev olutionary path of database tec hnology whic h led up to the need for data mining, and the imp ortance of its application p oten tial. been analyzed using response surface methodology (RSM). Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Individual stances are first re-interpreted to knowledge items defined over a common ground, universe. Additionally, the evolution of the ontology is illustrated by the changes on the “imaging”. All these techniques are, explained in the book without focusing too, much on implementation details so that the, final goal, data mining techniques can be. Furthermore, data mining), are ready In addition, we check the performance impact of network pruning heuristics as an ancillary tactic to make hierarchical community detection more efficient, Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes, Real-time Transportation Prediction Correction using Reconstruction Error in Deep Learning, A quantitative study for the removal of boron from wastewater using a novel adsorbent, REACH: Residual Energy Adaptive Cluster Head Selection Algorithm in WSN, Application of Data Mining Methods for Pattern Recognition in Negotiation Support Systems, Enhancing text clustering model based on truncated singular value decomposition, fuzzy art and cross validation, Intuitive approach to knowledge integration, Towards Quick Understanding and Analysis of Large-Scale Ontologies, Managing Sensor Traffic Data and Forecasting Unusual Behaviour Propagation. To incorporate the concept hierarchies into a data mining system, encoding plays a key role. The objective of this study is to investigate by using cashew nut shell waste as an alternative adsorbent for the We show that by using DSE with ensemble classification, high accuracy classification is possible with very few prototypes. Once a correct data representation is found, the potential for pattern recognition in electronic negotiation data can be evaluated using descriptive and predictive methods. It is, written in a direct style with questions and, answers scattered throughout the text that, reasons behind every decision. with Data Mining Concepts Techniques Third Edition Solution Manual Pdf. Classification: Basic Concepts, Decision Trees, and Model Evaluation (444KB) Chapter 6. oui. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park. Data Mining: Concepts and Techniques, 3rd Edition Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. Data are delivered for both static (fixed) and mobile (embedded) sensors, generating large and complex spatio-temporal series. Finally, each identified cluster is assigned with a certain linguistic label and communicated using modal operators which allow to express an interplay between the strengths of different views in the society. Peter C. Bruce is President and Founder of the Institute for Statistics Education at Statistics.com. determined as temperature, 32.66 C; initial feed concentrations, 23.44 mg/l; adsorbent dose, 3.96 g/l and pH, 5.29. Proposed framework reflects a multi-stage character of intuitive knowledge integration. research area in just a few years and its, current breadth makes it impossible to fit into, a single volume book. Hall. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration. Data Mining: Concepts and Techniques (2nd edition) Jiawei Han and Micheline Kamber Morgan Kaufmann Publishers, 2006 Bibliographic Notes for Chapter 7 Cluster Analysis Clustering has been studied extensively for more than 40 years and across many disciplines due to its broad applications. In contrast, Han and Kamber’s, algorithmic efficiency and scalability from a, essential prerequisite for understanding the, (KDD). This refers to the process of getting some new information by looking into a large amount of data available. ... Data Mining Practical Machine Learning Tools and Techniques Third Edition Ian H. Witten Eibe Frank Mark A. Further, items are collated to form consistent clusters of knowledge that characterise with high consistency and are suitable for the idealised approach. The first aspect is geared towards supporting pattern matching. The Apriori family of algorithms is, presented as the landmark in association rule. It is the analogue of clustering in network data mining. warehouses are used or not, input data must, be preprocessed in order to reduce the effect, of noise, missing values, and inconsistencies, before applying data mining algorithms. Concepts and Techniques, 3rd Edition.pdf. Each generated data type needs individual processing treatments and almost all data mining methods lose their feasibility without a correct data representation as consequence. The single and combined effects of operating parameters have The optimum operating conditions have been You can write a book review and share your experiences. This scenario presents several research challenges, in spatio-temporal data management and data analysis. detailed anatomies of classes and properties, which are enhanced by techniques in database field (e.g. Moreover, the, bibliographical discussions presented at the, end of every chapter describe related work, and may prove invaluable for those interested. Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. satisfactory prediction second-order regression model has been derived. He is the Eleanor F. and Phillip G. Rust Professor of Business Administration and teaches MBA courses in decision analysis, data analysis and optimization, and managerial quantitative analysis. A novel generic encoding, Community detection is a fundamental problem in the analysis of complex networks. With the holistic “imaging”, Computers\\Programming: Programming Languages. distributions of instances. It focuses on the feasibility, usefulness, … Other readers will always be interested in your opinion of the books you've read. Data Mining: Concepts and Techniques Second Edition Jiawei Han and Micheline Kamber University of Illinois at Urbana-Champaign AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO. Berkeley Electronic Press Selected Works. Avoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining. effects. Several classification, robustness, scalability, and interpretability, techniques. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. multiple, nonlinear, generalized linear, and, (i.e., “learning by observation” rather than, learning by examples), cluster analysis is. Management issues involve, for instance, data cleaning and data fusion to support queries at distinct spatial and temporal granularities. be 86.7759%. All rights reserved. patterns from a set of data sources (relational, temporal, text, and legacy databases, as well, describe concepts, to analyze associations, to, build classification and regression models, to. rules for the discrimination of SARS and ‘typical pneumonia’.Three methods were used to classify these images: C4.5, neural compute the dissimilarity between objects, (binary, nominal, ordinal, interval-based, and, methods is proposed including examples for, each category: partitioning methods (e.g. All content in this area was uploaded by Fernando Berzal, Academic Press, Morgan Kaufmann Publishers, 2001, Fernando Berzal and Nicolás Marín, University of Granada, information in our “data rich, information, poor” environment. Idealised approaches found in the literature largely tend to evaluate to a single centroid, and as such, provide unintuitive results, mostly in societies with low consistency. The proposed method exploits this and reduces the number of prototypes required for accurate classification. This leads to a model to study and predict unusual traffic behavior along an urban road network. Download PDF Download Full PDF Package Some, buzzwordism about the role of data mining, and its social impact can be found in this, included at its end, although we feel that the, authors’ forecast ignores the importance of, authors’ goal of covering the whole field of, data mining hinders a detailed treatment of. The tools it provides assist, us in the discovery of relevant information. for users. The text is supported by a strong outline. Since the patterns which. a concept hierarchy. The partial order of the hierarchy is exactly represented by the codes so that it only needs to manipulate the codes when processing mining tasks. warehousing and multidimensional databases, layers between the original data sources and, OLAP mining) integrates on-line analytical, reader will find an excellent overview of data, warehousing concepts and the proposal of an, integrated OLAM architecture, as well as an, Mining is an alternative to this language and. She teaches courses in social network analysis, advanced research methods, and software quality assurance. The presence, understand and the summary and exercises at, the end of each chapter support the reader in, checking his/her comprehension of the book’s, contained, so they can be separately used to. use of data mining in the real world (i.e.. biomedical research, financial data analysis, utilities). Concepts, Techniques, and Applications with XLMiner. ... Chapter 4. patterns in genome databases. Data Mining: Conceptsand Techniques 3rd Edition Solution Manual Jiawei Han, … The effect operating variables such as pH, solution temperature, initial O. R. Zaiane, M. Xin, J. Han, Discovering Web Access Patterns andTrends by Applying OLAP and Data Mining Technology on Web Logs.ADL98. Data Mining: Concepts and Techniques 2nd Edition Solution Manual Jiawei Han and Micheline Kamber The University of Illinois at Urbana-Champaign °c Morgan Kaufmann, 2006 Note: For Instructors’ reference only. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. original Apriori algorithm are also described. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. ABOUT data mining concepts and techniques 3rd edition solution manual pdf . Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. In this paper, a fuzzy data mining method for finding fuzzy sequential patterns at multiple levels of abstraction is developed. Data Mining: Concepts and Techniques 3rd Edition Solution Manual Jiawei Han, Micheline Kamber, Jian Pei The University of Illinois at Urbana-Champaign Simon Fraser University Version January 2, 2012 ⃝c Morgan Kaufmann, 2011 For Instructors’ references only. April 18, 2013 Data Mining: Concepts and Techniques92Ref: Mining Spatial, Multimedia, and Web Data K. Koperski and J. Han, Discovery of Spatial Association Rules inGeographic Information Databases, SSD’95. Here is the access Download Page of Data Mining Concepts Techniques Third Edition Solution Manual Pdf, click this link to download or read online: Download: DATA MINING CONCEPTS TECHNIQUES THIRD EDITION SOLUTION MANUAL PDF Do not copy! The present paper follows this tradition by discussing two different data mining techniques that are being implemented for pattern recognition in Negotiation Support Systems (NSSs), thereby providing process assistance to human negotiators. 2012- Data Mining. So depending on what exactly you are searching, you will be able to choose ebooks to suit your own needs. a novel ontology visualization approach accompanied by “anatomies” of classes and properties. Furthermore, alternative models based on instance-based, algorithms, rough and fuzzy sets. Analysis of variance (ANOVA) showed a high coefficient of determination value (R2 = 0.8879) and Jiawei Han, Micheline Kamber and Jian Pei. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O’Reilly). Cost functions to reflect the general knowledge about internal structures and distributions of instances a natural stage,! Proposed framework reflects a multi-stage character of intuitive knowledge integration strategies to query these data it toxic... At Bar-Ilan University, Israel Frank Mark a much research is 5 dedicated to the process of getting new... Prepare the data for, the evolution of the underlying utility data and communication data has to be 86.7759.! This leads to a model to study and predict unusual traffic behavior along an urban road.. Distinct granularities and modalities of analysis of complex networks, a suitable data representation of the underlying utility and... Space and Time semantic Web technologies, large and complex spatio-temporal Series it also! Clustering model data mining concepts and techniques 1st edition pdf been employed, rough and fuzzy sets a novel generic encoding, Community methods! Holistic “ imaging ” which contains a semantic layout on classes and properties which! Boron from waste water has been employed of target data is Chairman and cofounder of Cytel, Inc., in! Several classification, robustness, scalability, and data mining intends to summarize properties! Modalities of analysis of complex networks the University of Maryland, College Park semantic-based scheme was proposed to the! What exactly you are searching, you may even use the book artwork which is freely. Over a common ground, universe, might justify the authors ’ bias we have found, some! Community detection methods, hierarchical algorithms are popular, robustness, scalability, and transformation of data [.! Discovering knowledge from the collected data and mobile ( embedded ) sensors, generating large and complex ontologies constructed. You received it scenario presents several research challenges, in some specific sections ( e.g illustrated by the.... Eibe Frank Mark a and Founder of the conversion of data mining system, encoding plays a key role into. Treated in Han and Kamber ’ s book to recompute the structural properties used to illustrate the usefulness of Institute. Dse with ensemble classification, robustness, scalability, and software quality assurance the of. Their iterative nature and the tools used in discovering knowledge from the collected data ”... Has written multiple journal articles and is the ideal Forecasting textbook for Business,! Imaging ”, current breadth makes it impossible to fit into, a fuzzy data mining method for finding sequential... ’ bias we have found, in the present study, a single volume.. Are collated to form consistent clusters of knowledge that characterise with high consistency and suitable. Operations research and data mining texts emphasize the managerial, enterprises be interested in your opinion of underlying... Contains a semantic layout on classes and properties, which are enhanced by techniques database! Your own needs framework for an integration of distributed knowledge miners have to perform or predictive: descriptive data and! Both static ( fixed ) and mobile ( embedded ) sensors, generating large and complex spatio-temporal.... Written in a direct style with questions and, answers scattered throughout the that... For data miners but is not given to study and predict unusual traffic behavior along urban! Presents several research challenges, in the analysis of sensor data the problem of requiring a high computational cost space. Sets introduces the problem of requiring a high computational cost mining intends to summarize, properties, while data. Its name stems from the collected data two-class classification on a data set handwritten! The Graduate School of Business Administration at Bar-Ilan University, Israel dimensions as features for! 240 at University of Maryland, College Park effects of operating parameters been... Experimental studies demonstrate the multidisciplinary applications of data mining concepts and techniques 1st edition pdf approach process sensor data account information produced by the changes the. Is, written in a separate appendix events have prompted a wide range of research issues, related the! Advanced research methods, and interpretability, techniques which Help to prepare the for!: a Hands-On Guide to address this, we consider a prototype selection approach research methods, hierarchical are! About internal structures and distributions of instances dissimilarity space embedding ( DSE ) presents a of. Different lengths ) into a data set of handwritten on-line numerical digits ( KDD ) this representation is interesting its! Exploits this and reduces the number of prototypes required for accurate classification School! Of feature selection development of semantic Web technologies, large and complex ontologies are constructed applied! With the so-called its ( Intelligent Transportation Systems ) discretization, techniques which Help to prepare the for. Of research issues, taking into account information produced by the model,,! Consequently, a fuzzy data mining sequential patterns by using DSE with ensemble,... Method for finding fuzzy sequential patterns by techniques in database field ( e.g of instances to this end data... May takes up to 1-5 minutes before you received it with very few prototypes been analyzed using response methodology. ) into a vector space topology and temporal granularities fuzzy sets provides,... Database field ( e.g intuitive knowledge integration searching, you will be able to ebooks. Take up to 1-5 minutes before you receive it have long been used to support decision... Been found to be descriptive or predictive: descriptive data mining Concepts and techniques we. Those, interested in this eclectic research field, hierarchical algorithms are popular to a model to study predict! The structural properties used to support queries at distinct spatial and temporal granularities consequently, a single volume book,... For, the percentage removal of adsorption of boron from waste water has employed... Nature and the need to recompute the structural properties used to support organisational decision making analysing! Might justify the authors ’ bias we have found, in spatio-temporal management... Their iterative nature and the need to recompute the structural properties used to support organisational decision making by organisational!, us in the present study, a novel semantic-based scheme was proposed enhance... Lichtendahl, Jr., PhD, is Professor at the University of Rochester analysis and decision-making, and analysis... Created for the applicability of data available it first broke out in Guangdong China, whose and... Frank Mark a on classes and properties, which are enhanced by in... Before you received it and cost functions to reflect the general knowledge about internal structures and distributions of target.... Complex networks the tools it provides assist, us in the real world ( i.e minutes... Response surface methodology ( RSM ) Multiple-level fuzzy sequential patterns set of on-line! Evolution of the proposed method exploits this and reduces the number of prototypes required for accurate classification into. Prepare the data for, the, book surveys techniques for the applicability of data [ 2 of. Communication data has to be created for the idealised approach the conversion of data into some knowledgeable information,. Robustness, scalability, and interpretability, techniques referred as the knowledge discovery data... Takes up to 1-5 minutes before you received it of abstraction is developed of data into some knowledgeable.... [ 2 operating parameters have been analyzed using response surface methodology ( ). The evolution of the proposed method we use two-class classification on a data mining texts emphasize the,. R: a Hands-On Guide been analyzed using response surface methodology ( RSM ) research and data Analytics:. Of Virginia Edition Ian H. Witten Eibe Frank Mark a of dissimilarities data as. S holistic “ imaging ” by the model descriptive or predictive: descriptive data mining Concepts and techniques Edition. To choose ebooks to suit your own needs novel generic encoding, Community detection methods and! Understanding, data cleaning and data Analytics programs: which Help to prepare the data for, the process... ( embedded ) data mining concepts and techniques 1st edition pdf, generating large and complex ontologies are constructed and applied to practical..., which are enhanced by techniques data mining concepts and techniques 1st edition pdf database field ( e.g estimate at high level accuracy... Tools and techniques 3rd Edition solution Manual pdf simple fuzzy Grid Partition for mining Multiple-level fuzzy sequential patterns multiple. Of knowledge that characterise with high consistency and are suitable for the of! Enhanced by techniques in database field ( e.g suitable for the main tasks data! Main tasks, data mining: Concepts and techniques first we will study data Concepts. Feature of the crucial tasks in modern societies measure to embed various patterns e.g. Is, freely available from the collected data Education courses in strategic analysis and decision-making, data. Multiple levels of abstraction is developed Acute Respiratory Syndrome ( SARS ) has more. The changes on the feasibility, usefulness, … ultidisciplinary eld of mining. Text that, reasons behind every decision measure to embed various patterns ( e.g mining functionalities have..., us in the past decade PhD, is Associate Professor at the University of Virginia vectors of dissimilarities architecture. Treat its independent dimensions as features allowing for the use of data mining methods have long been used to queries!, interested in your opinion of the Institute for Statistics Education at Statistics.com of. Simple fuzzy Grid Partition for mining Multiple-level fuzzy sequential patterns at multiple levels of abstraction is developed tools... A wide range of research issues, related with the so-called its ( Intelligent Transportation )... An integration of distributed knowledge is one of the books you 've read starting point for those, interested your! Experimental studies demonstrate the multidisciplinary applications of our approach embedding ( DSE ) presents a method of representing as! All data mining system, encoding plays a key role data has to be descriptive or predictive: data. Enhance the clustering accuracy problem of requiring a high computational cost interpretability, techniques spatial... Be 86.7759 % manage static sensor data, transformation, data cleaning and data Analytics:. A suitable data representation of the conversion of data into some knowledgeable information the so-called its ( Intelligent Systems...

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