Improving Knowledge Discovery through the Integration of Data Mining Techniques

Improving Knowledge Discovery through the Integration of Data Mining Techniques

Copyright: © 2015 |Pages: 391

ISBN13: 9781466685130|ISBN10: 1466685131|EISBN13: 9781466685147

DOI: 10.4018/978-1-4666-8513-0

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Usman, Muhammad, editor. Improving Knowledge Discovery through the Integration of Data Mining Techniques. IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8513-0

APA

Usman, M. (Ed.). (2015). Improving Knowledge Discovery through the Integration of Data Mining Techniques. IGI Global. https://doi.org/10.4018/978-1-4666-8513-0

Chicago

Usman, Muhammad, ed. Improving Knowledge Discovery through the Integration of Data Mining Techniques. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8513-0

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Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery.

Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.

Table of Contents

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Front Materials

Title Page

Copyright Page

Advances in Data Mining and Database Management (ADMDM) Book Series

Editorial Advisory Board

Foreword

Preface

Chapters

Section 1

Chapter 1

Integration of Data Mining and Statistical Methods for Constructing and Exploring Data Cubes (pages 1-12)

Muhammad Usman

In high dimensional environments, the sheer size and volume of data poses a number of challenges in order to generate meaningful and informative data cubes. Data cube construction and exploration is a manual process in which analysts...

Chapter 2

Online Processing of End-User Data in Real-Time Data Warehousing (pages 13-31)

M. Asif Naeem, Noreen Jamil

Stream-based join algorithms are a promising technology for modern real-time data warehouses. A particular category of stream-based joins is a semi-stream join where a single stream is joined with a disk based master data. The join...

Chapter 3

Multi-Relational Data Mining A Comprehensive Survey (pages 32-53)

Ali H. Gazala, Waseem Ahmad

Multi-Relational Data Mining or MRDM is a growing research area focuses on discovering hidden patterns and useful knowledge from relational databases. While the vast majority of data mining algorithms and techniques look for patterns...

Chapter 4

Comparative Study of Incremental Learning Algorithms in Multidimensional Outlier Detection on Data Stream (pages 54-73)

Simon Fong, Dong Han, Athanasios V. Vasilakos

Multi-dimensional outlier detection (MOD) over data streams is one of the most significant data stream mining techniques. When multivariate data are streaming in high speed, outliers are to be detected efficiently and accurately....

Section 2

Chapter 5

Advances of Applying Metaheuristics to Data Mining Techniques (pages 75-103)

Simon Fong, Jinyan Li, Xueyuan Gong, Athanasios V. Vasilakos

Metaheuristics have lately gained popularity among researchers. Their underlying designs are inspired by biological entities and their behaviors, e.g. schools of fish, colonies of insects, and other land animals etc. They have been...

Chapter 6

Artificial Immune Optimization Algorithm (pages 104-123)

Waseem Ahmad

Artificial immune system (AIS) is a paradigm inspired by processes and metaphors of natural immune system (NIS). There is a rapidly growing interest in AIS approaches to machine learning and especially in the domain of optimization....

Chapter 7

The Role of Hypermutation and Affinity Maturation in AIS Approaches to Clustering (pages 124-144)

Waseem Ahmad, Ajit Narayanan

In recent years, several artificial immune system (AIS) approaches have been proposed for unsupervised learning. Generally, in these approaches antibodies (or B-cells) are considered as clusters and antigens are data samples or...

Chapter 8

Cancer Pathway Network Analysis Using Cellular Automata (pages 145-159)

Kalyan Mahata, Anasua Sarkar

Identification of cancer pathways is the central goal in the cancer gene expression data analysis. Data mining refers to the process analyzing huge data in order to find useful pattern. Data classification is the process of...

Section 3

Chapter 9

Knowledge Extraction from Information System Using Rough Computing (pages 161-182)

D. P. Acharjya

The amount of data collected across various sources in real life situations is never in its complete form. That is, it is never precise or it never gives definite knowledge. It always contains uncertainty and vagueness. Therefore...

Chapter 10

Data Mining Techniques on Earthquake Data: Recent Data Mining Approaches (pages 183-199)

Negar Sadat Soleimani Zakeri, Saeid Pashazadeh

Active faults are sources of earthquakes and one of them is north fault of Tabriz in the northwest of Iran. The activation of faults can harm humans' life and constructions. The analysis of the seismic data in active regions can...

Chapter 11

An Evaluation of C4.5 and Fuzzy C4.5 with Effect of Pruning Methods (pages 200-232)

Tayyeba Naseer, Sohail Asghar

Classification is a supervised learning technique in data mining classify historical data. The decision tree is easy method for inductive inference. The decision tree induction process has three major steps – first complete decision...

Chapter 12

An Empirical Evaluation of Feature Selection Methods (pages 233-258)

Mohsin Iqbal, Saif Ur Rehman, Saira Gillani, Sohail Asghar

The key objective of the chapter would be to study the classification accuracy, using feature selection with machine learning algorithms. The dimensionality of the data is reduced by implementing Feature selection and accuracy of the...

Section 4

Chapter 13

Data Mining Driven Rule Based Expert System for Medical Billing Compliance: A Case Study (pages 260-288)

Umair Abdullah, Aftab Ahmed, Sohail Asghar, Kashif Zafar

Most of the data mining projects generate information (summarized in the form of graphs and charts) for business executives and decision makers; however it leaves to the choice of decision makers either to use it or disregard it. The...

Chapter 14

A Web Backtracking Technique for Fraud Detection in Financial Applications (pages 289-307)

Tasawar Hussain, Sohail Asghar

The web based applications are maturing and gaining the confidence of their users gradually, however, www still lacks the mechanism to stop the hackers. The implementing the adhesive security measures such as intrusion deduction...

Chapter 15

Segmentation of Crops and Weeds Using Supervised Learning Technique (pages 308-333)

Noureen Zafar, Saif Ur Rehman, Saira Gillani, Sohail Asghar

In this article, segmentation of weeds and crops has been investigated by using supervised learning based on feed forward neural network. The images have been taken from the satellite imaginary for a specified region on the...

Chapter 16

A Supervised Learning Model for AGV Perception in Unstructured Environment (pages 334-353)

Rizwan Aqeel, Saif Ur Rehman, Saira Gillani, Sohail Asghar

This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, which is a vehicle that can navigate without human supervision. AGV navigation over an unstructured road is a challenging task and is...

Back Materials

Compilation of References

About the Contributors

Index

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