The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * 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 real business data

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Data Mining: Concepts and Techniques

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.

https://courses.washington.edu/geog482/resource/14_Beyond_Accuracy.pdf

Beyond accuracy: what data quality means to data consumers

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

https://andreashellander.se/wp-content/uploads/2016/01/1-s2.0-S0306437914001288-main.pdf

The rise of big data on cloud computing

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.

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Data quality assessment

As practitioners know, creating awareness of a problem and its impact is a critical first step toward resolution of the problem [5]. The needed awareness of poor data quality, while growing, has not yet been achieved in many enterprises. After all, the typical executive is already besieged by too many problems, low customer satisfaction, high costs, a data warehouse project that is late, and so forth. This article aims to increase awareness by providing a summary of the impacts of poor data quality on a typical enterprise. These impacts Thomas C. Redman

The impact of poor data quality on the typical enterprise

From the Publisher:
Written by the inventor of many modern techniques for data quality, this comprehensive book provides business leaders, process owners, and information professionals with the background and methods necessary to set up a data quality program, make and sustain order of magnitude improvements, and create a unique and important business advantage.

Data Quality For The Information Age

Data Quality: The Field Guide

The rapid proliferation of computer-based information systems is increasing the importance of data quality to both system makers and users. However, there is neither an established framework nor common terminology for investigating data quality. There is not even agreement on what the term “data” means. We lay a foundation for the study of data quality in this paper. In the first part of the paper we discuss five approaches to defining “data” in the literature. We then propose an approach especially conducive to discussing data quality. In the second part of the paper we discuss the most important dimensions of data quality: accuracy, completeness, consistency and currentness. We define these four and several related dimensions and discuss them in detail. We close the paper by outlining several areas for further research on data quality.

The notion of data and its quality dimensions

Errors in data can cost a company millions of dollars, alienate customers, and make implementing new strategies difficult or impossible The author describes a process AT&T uses to recognize poor data and improve their quality He proposes a three-step method for identifying data-quality problems, treating data as an asset, and applying quality systems to the processes that create data

Thomas C. Redman

Papers

The impact of poor data quality on the typical enterprise

Data Quality For The Information Age

Data Quality: The Field Guide

The notion of data and its quality dimensions

Improve Data Quality for Competitive Advantage