Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining ...and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields * Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic ...and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. * Written "By Practitioners for Practitioners" * Non-technical explanations build understanding without jargon and equations * Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models * Practical advice from successful real-world implementations * Includes extensive case studies, examples, MS PowerPoint slides and datasets * CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book
•The work extends prior educational data mining (EDM) reviews and updates its history.•A data mining (DM) profile is set to depict the DM ground that supports EDM works.•An EDM approach profile is ...set to provide key traits for depicting EDM approaches.•A trait-value pattern is set to depict most of descriptive and predictive EDM models.•Three disciplines, tasks, methods, algorithms are the most used for building EDM works.
This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Reviewing limitations of previous data-mining based FDD methods on HVAC systems.•Knowledge-based and model-based systems have limitations in complicated FDD process.•In FDD in large-scale HVAC ...systems, driven methods are superior to other methods.•Hybrid methods have greatpotential in finding naïve faults in FDD process.•Developed hybrid methods can overcome delays, and get less false and missed alarms.
Abnormal operation of HVAC systems can result in an increase in energy usage as well as poor indoor air quality, thermal discomfort, and low productivity. Building automated systems (BAS) collects a massive amount of data related to the operation of each component of HVAC systems. Although BAS has been implemented in many buildings over the past decade, the collected data have not been analyzed thoroughly. Some studies have relied on data-mining methods to predict, detect, and diagnose faults in HVAC systems. This paper critically reviews the existing literature and identifies the research gaps in data-driven data mining fault detection and diagnosis (FDD) methods studies on HVAC systems. In this review, data-driven based FDD methods are classified into three classes, namely supervised, unsupervised, and hybrid-learning methods. The hybrid approaches are introduced as the preferred methods among the existing approaches to be used in online FDD processes. Furthermore, some components of HVAC systems and their potential faults are discussed in detail. The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones. Moreover, an optimal approach could involve both supervised and unsupervised learning (hybrid methods).
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
