Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends

Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends

David Taniar, Lukman Hakim Iwan

Copyright: © 2012 |Pages: 465

ISBN13: 9781613504741|ISBN10: 1613504748|EISBN13: 9781613504758

DOI: 10.4018/978-1-61350-474-1

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MLA

Taniar, David, and Lukman Hakim Iwan, editors. Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends. IGI Global, 2012. https://doi.org/10.4018/978-1-61350-474-1

APA

Taniar, D. & Iwan, L. H. (Eds.). (2012). Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends. IGI Global. https://doi.org/10.4018/978-1-61350-474-1

Chicago

Taniar, David, and Lukman Hakim Iwan, eds. Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-474-1

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Data mining is still a relatively young field, expanding at the rate of technology while advancing tools and techniques for gaining knowledge, finding patterns, and managing databases.

Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends is an updated look at the state of technology in the field of data mining and analytics. As processor speeds, database size, network capabilities, artificial intelligence, and most fields of hardware and software continue to improve at a staggering rate of increased capability and pace, it is vital for practitioners to stay abreast of the current issues and research in the field. This volume is perfect for IT specialists, data analysts, practitioners and academics alike, offering the latest technological, analytical, ethical, and commercial perspectives on topics in data mining.

Table of Contents

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Front Materials

Title Page

Copyright Page

Preface

Chapters

Chapter 1

Investigating the Properties of a Social Bookmarking and Tagging Network (pages 1-16)

Ralitsa Angelova, Marek Lipczak, Evangelos Milios, Pawel Pralat

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests....

Chapter 2

The Dynamics of Content Popularity in Social Media (pages 17-33)

Symeon Papadopoulos, Athena Vakali, Ioannis Kompatsiaris

Social Bookmarking Systems (SBS) have been widely adopted in the last years, and thus they have had a significant impact on the way that online content is accessed, read and rated. Until recently, the decision on what content to...

Chapter 3

Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor (pages 34-51)

Robert Wetzker, Carsten Zimmermann, Christian Bauckhage

The authors present and evaluate an approach to trend detection in social bookmarking systems using a probabilistic generative model in combination with smoothing techniques. Social bookmarking systems are gaining major interest...

Chapter 4

Mining Frequent Generalized Patterns for Web Personalization in the Presence of Taxonomies (pages 52-68)

Panagiotis Giannikopoulos, Iraklis Varlamis, Magdalini Eirinaki

The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the...

Chapter 5

An Efficient Method for Discretizing Continuous Attributes (pages 69-90)

Kelley M. Engle, Aryya Gangopadhyay

In this paper the authors present a novel method for finding optimal split points for discretization of continuous attributes. Such a method can be used in many data mining techniques for large databases. The method consists of two...

Chapter 6

Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy (pages 91-109)

Amit Saxena, John Wang

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of...

Chapter 7

Graph-Based Modelling of Concurrent Sequential Patterns (pages 110-127)

Jing Lu, Weiru Chen, Malcolm Keech

Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a...

Chapter 8

User-Centric Similarity and Proximity Measures for Spatial Personalization (pages 128-146)

Yanwu Yang, Christophe Claramunt, Marie-Aude Aufaure, Wensheng Zhang

Spatial personalization can be defined as a novel way to fulfill user information needs when accessing spatial information services either on the web or in mobile environments. The research presented in this paper introduces a...

Chapter 9

Estimating Semi-Parametric Missing Values with Iterative Imputation (pages 147-156)

Shichao Zhang

In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there...

Chapter 10

Exploring Disease Association from the NHANES Data: Data Mining, Pattern Summarization, and Visual Analytics (pages 157-173)

Zhengzheng Xing, Jian Pei

Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using...

Chapter 11

Classification of Peer-to-Peer Traffic Using a Two-Stage Window-Based Classifier with Fast Decision Tree and IP Layer Attributes (pages 174-188)

Bijan Raahemi, Ali Mumtaz

This paper presents a new approach using data mining techniques, and in particular a two-stage architecture, for classification of Peer-to-Peer (P2P) traffic in IP networks where in the first stage the traffic is filtered using...

Chapter 12

Constrained Cube Lattices for Multidimensional Database Mining (pages 189-218)

Alain Casali, Sébastien Nedjar, Rosine Cicchetti, Lotfi Lakhal

In multidimensional database mining, constrained multidimensional patterns differ from the well-known frequent patterns from both conceptual and logical points of view because of a common structure and the ability to support various...

Chapter 13

ASCCN: Arbitrary Shaped Clustering Method with Compatible Nucleoids (pages 219-232)

Renxia Wan, Lixin Wang, Xiaoke Su

Special clustering algorithms are attractive for the task of grouping an arbitrary shaped database into several proper classes. Until now, a wide variety of clustering algorithms for this task have been proposed, although the...

Chapter 14

A New Similarity Metric for Sequential Data (pages 233-248)

Pradeep Kumar, Bapi S. Raju, P. Radha Krishna

In many data mining applications, both classification and clustering algorithms require a distance/similarity measure. The central problem in similarity based clustering/classification comprising sequential data is deciding an...

Chapter 15

When Spatial Analysis Meets OLAP: Multidimensional Model and Operators (pages 249-277)

Sandro Bimonte, Anne Tchounikine, Maryvonne Miquel, François Pinet

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and...

Chapter 16

Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis (pages 278-289)

Yongsong Qin, Shichao Zhang, Chengqi Zhang

The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid...

Back Materials

Compilation of References

About the Contributors

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