Quality Measures in Data Mining
Author: Fabrice Guillet
Publisher: Springer Science & Business Media
Published: 2007-01-08
Total Pages: 319
ISBN-13: 3540449116
DOWNLOAD EBOOK →This book presents recent advances in quality measures in data mining.
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Uncertainty Handling and Quality Assessment in Data Mining
Author: Michalis Vazirgiannis
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 231
ISBN-13: 144710031X
DOWNLOAD EBOOK →The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.
The Practitioner's Guide to Data Quality Improvement
Author: David Loshin
Publisher: Elsevier
Published: 2010-11-22
Total Pages: 432
ISBN-13: 9780080920344
DOWNLOAD EBOOK →The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.
Knowledge Discovery and Data Mining: Challenges and Realities
Author: Zhu, Xingquan
Publisher: IGI Global
Published: 2007-04-30
Total Pages: 290
ISBN-13: 1599042541
DOWNLOAD EBOOK →"This book provides a focal point for research and real-world data mining practitioners that advance knowledge discovery from low-quality data; it presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying low-quality data. Contributions also focus on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing"--Provided by publisher.
Integration Challenges for Analytics, Business Intelligence, and Data Mining
Author: Azevedo, Ana
Publisher: IGI Global
Published: 2020-12-11
Total Pages: 250
ISBN-13: 1799857832
DOWNLOAD EBOOK →As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.
Advances in Knowledge Discovery and Management
Author: Fabrice Guillet
Publisher: Springer
Published: 2016-11-03
Total Pages: 278
ISBN-13: 3319457632
DOWNLOAD EBOOK →This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.
Quality Aspects in Spatial Data Mining
Author: Alfred Stein
Publisher: CRC Press
Published: 2016-04-19
Total Pages: 378
ISBN-13: 1420069276
DOWNLOAD EBOOK →Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data QualitySubstantial progress has been made toward developing effective techniques for spatial information processing in recent years. This science deals with models of reality in a GIS, however, and not with reality itself. Therefore, spatial information processes are often impre
Trends and Applications in Knowledge Discovery and Data Mining
Author: Jiuyong Li
Publisher: Springer
Published: 2013-08-23
Total Pages: 571
ISBN-13: 3642403190
DOWNLOAD EBOOK →This book constitutes the refereed proceedings at PAKDD Workshops 2013, affiliated with the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) held in Gold Coast, Australia in April 2013. The 47 revised full papers presented were carefully reviewed and selected from 92 submissions. The workshops affiliated with PAKDD 2013 include: Data Mining Applications in Industry and Government (DMApps), Data Analytics for Targeted Healthcare (DANTH), Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE), Biologically Inspired Techniques for Data Mining (BDM), Constraint Discovery and Application (CDA), Cloud Service Discovery (CloudSD).
Data Mining Algorithms
Author: Pawel Cichosz
Publisher: John Wiley & Sons
Published: 2015-01-27
Total Pages: 717
ISBN-13: 111833258X
DOWNLOAD EBOOK →Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.
Data Preparation for Data Mining
Author: Dorian Pyle
Publisher: Morgan Kaufmann
Published: 1999-03-22
Total Pages: 566
ISBN-13: 9781558605299
DOWNLOAD EBOOK →This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.
