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

Basics of qualitative research: Grounded theory procedures and techniques

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)

Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases In this paper, we explore whether clustering methods have a role to play in spatial data mining To this end, we develop a new clustering method called CLAHANS which is based on randomized search We also develop two spatial data mining algorithms that use CLAHANS Our analysis and experiments show that with the assistance of CLAHANS, these two algorithms are very effective and can lead to discoveries that are difficult to find with current spatial data mining algorithms Furthermore, experiments conducted to compare the performance of CLAHANS with that of existing clustering methods show that CLAHANS is the most efficient

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Efficient and Effective Clustering Methods for Spatial Data Mining

Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that overlap a given search region). More than ten years of spatial database research have resulted in a great variety of multidimensional access methods to support such operations. We give an overview of that work. After a brief survey of spatial data management in general, we first present the class of point access methods, which are used to search sets of points in two or more dimensions. The second part of the paper is devoted to spatial access methods to handle extended objects, such as rectangles or polyhedra. We conclude with a discussion of theoretical and experimental results concerning the relative performance of various approaches.

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Multidimensional access methods

In times of ubiquitous electronic communication and increasing industry pressure for standard electronic authentication, the maintenance of privacy, or "the right to be left alone" becomes a subject of increasing concern. The possibility of a "transparent human," whose vital information is up for grabs, can most easily be envisioned in the realm of e-commerce, due in part to the large amounts of data available, and in part to the high payoffs expected from using this data for marketing purposes.

Privacy in e-commerce: stated preferences vs. actual behavior

Consumers need to feel they have control over the RFID infrastructure before they routinely trust its services.

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RFID and the perception of control: the consumer's view

Social Network Sites (SNSs) rely exclusively on user-generated content to offer engaging and rewarding experience to its members As a result, stimulating user communication and self-disclosure is vital for the sustainability of SNSs However, considering that the SNS users are increasingly culturally diverse, motivating this audience to self-disclose requires understanding of their cultural intricacies Yet existing research offers only limited insights into the role of culture behind the motivation of SNS users to self-disclose Building on the privacy calculus framework, this study explores the role of two cultural dimensions – individualism and uncertainty avoidance – in self-disclosure decisions of SNS users Survey responses of US and German Facebook members are used as the basis for our analysis Structural equation modeling and multi-group analysis results reveal the distinct role of culture in the cognitive patterns of SNS users The authors find that trusting beliefs play a key role in the self-disclosure decisions of users from individualistic cultures At the same time, uncertainty avoidance determines the impact of privacy concerns This paper contributes to the theory by rejecting the universal nature of privacy calculus processes The findings provide for an array of managerial implications for SNS providers as they strive to encourage content creation and sharing by their heterogeneous members

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Self-disclosure and Privacy Calculus on Social Networking Sites: The Role of Culture - Intercultural Dynamics of Privacy Calculus

Driven by privacy-related fears, users of Online Social Networks may start to reduce their network activities. This trend can have a negative impact on network sustainability and its business value. Nevertheless, very little is understood about the privacy-related concerns of users and the impact of those concerns on identity performance. To close this gap, we take a systematic view of user privacy concerns on such platforms. Based on insights from focus groups and an empirical study with 210 subjects, we find that (i) Organizational Threats and (ii) Social Threats stemming from the user environment constitute two underlying dimensions of the construct “Privacy Concerns in Online Social Networks”. Using a Structural Equation Model, we examine the impact of the identified dimensions of concern on the Amount, Honesty, and Conscious Control of individual self-disclosure on these sites. We find that users tend to reduce the Amount of information disclosed as a response to their concerns regarding Organizational Threats. Additionally, users become more conscious about the information they reveal as a result of Social Threats. Network providers may want to develop specific mechanisms to alleviate identified user concerns and thereby ensure network sustainability.

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Oliver Günther

Papers

Multidimensional access methods

Privacy in e-commerce: stated preferences vs. actual behavior

RFID and the perception of control: the consumer's view

Self-disclosure and Privacy Calculus on Social Networking Sites: The Role of Culture - Intercultural Dynamics of Privacy Calculus

Privacy concerns and identity in online social networks