Pattern Discovery Using Sequence Data Mining: Applications and Studies

Pattern Discovery Using Sequence Data Mining: Applications and Studies

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

Copyright: © 2012 |Pages: 286

ISBN13: 9781613500569|ISBN10: 1613500564|EISBN13: 9781613500576

DOI: 10.4018/978-1-61350-056-9

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MLA

Kumar, Pradeep, et al., editors. Pattern Discovery Using Sequence Data Mining: Applications and Studies. IGI Global, 2012. https://doi.org/10.4018/978-1-61350-056-9

APA

Kumar, P., Krishna, P., & Raju, S. (Eds.). (2012). Pattern Discovery Using Sequence Data Mining: Applications and Studies. IGI Global. https://doi.org/10.4018/978-1-61350-056-9

Chicago

Kumar, Pradeep, P. Radha Krishna, and S. Bapi Raju, eds. Pattern Discovery Using Sequence Data Mining: Applications and Studies. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-056-9

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Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios.

Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.

Table of Contents

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Front Materials

Title Page

Copyright Page

List of Reviewers

Preface

Chapters

Current State of Art

Chapter 1

Applications of Pattern Discovery Using Sequential Data Mining (pages 1-23)

Manish Gupta, Jiawei Han

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such...

Chapter 2

A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I: Sequences of Continuous Feature Vectors (pages 24-50)

Dileep A. D., Veena T., C. Chandra Sekhar

Sequential data mining involves analysis of sequential patterns of varying length. Sequential pattern analysis is important for pattern discovery from sequences of discrete symbols as in bioinformatics and text analysis, and from...

Chapter 3

A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part II: Sequences of Discrete Symbols (pages 51-71)

Veena T., Dileep A. D., C. Chandra Sekhar

Pattern analysis tasks on sequences of discrete symbols are important for pattern discovery in bioinformatics, text analysis, speech processing, and handwritten character recognition. Discrete symbols may correspond to amino acids or...

Techniques

Chapter 4

Mining Statistically Significant Substrings Based on the Chi-Square Measure (pages 73-82)

Sourav Dutta, Arnab Bhattacharya

With the tremendous expansion of reservoirs of sequence data stored worldwide, efficient mining of large string databases in various domains including intrusion detection systems, player statistics, texts, and proteins, has emerged...

Chapter 5

Unbalanced Sequential Data Classification using Extreme Outlier Elimination and Sampling Techniques (pages 83-93)

T.Maruthi Padmaja, Raju S. Bapi, P. Radha Krishna

Predicting minority class sequence patterns from the noisy and unbalanced sequential datasets is a challenging task. To solve this problem, we proposed a new approach called extreme outlier elimination and hybrid sampling technique....

Chapter 6

Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications (pages 94-110)

T. Ravindra Babu, M. Narasimha Murty, S. V. Subrahmanya

Data Mining deals with efficient algorithms for dealing with large data. When such algorithms are combined with data compaction, they would lead to superior performance. Approaches to deal with large data include working with...

Chapter 7

Classification of Biological Sequences (pages 111-135)

Pratibha Rani, Vikram Pudi

The rapid progress of computational biology, biotechnology, and bioinformatics in the last two decades has led to the accumulation of tremendous amounts of biological data that demands in-depth analysis. Data mining methods have been...

Applications

Chapter 8

Approaches for Pattern Discovery Using Sequential Data Mining (pages 137-154)

Manish Gupta, Jiawei Han

In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in literature. Apriori based methods and the pattern growth methods are the earliest and the...

Chapter 9

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques (pages 155-165)

S. Prasanthi, S.Durga Bhavani, T. Sobha Rani, Raju S. Bapi

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the...

Chapter 10

Identification of Genomic Islands by Pattern Discovery (pages 166-181)

Nita Parekh

Pattern discovery is at the heart of bioinformatics, and algorithms from computer science have been widely used for identifying biological patterns. The assumption behind pattern discovery approaches is that a pattern that occurs...

Chapter 11

Video Stream Mining for On-Road Traffic Density Analytics (pages 182-194)

Rudra Narayan Hota, Kishore Jonna, P. Radha Krishna

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating...

Chapter 12

Discovering Patterns in Order to Detect Weak Signals and Define New Strategies (pages 195-211)

Anass El Haddadi, Bernard Dousset, Ilham Berrada

Competitive intelligence activities rely on collecting and analyzing data in order to discover patterns from data using sequence data mining. The discovered patterns are used to help decision-makers considering innovation and...

Chapter 13

Discovering Patterns for Architecture Simulation by Using Sequence Mining (pages 212-236)

Pinar Senkul, Nilufer Onder, Soner Onder, Engin Maden, Hui Meen Nyew

The goal of computer architecture research is to design and build high performance systems that make effective use of resources such as space and power. The design process typically involves a detailed simulation of the proposed...

Chapter 14

Sequence Pattern Mining for Web Logs (pages 237-243)

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

Interestingness measures play an important role in finding frequently occurring patterns, regardless of the kind of patterns being mined. In this work, we propose variation to the AprioriALL Algorithm, which is commonly used for the...

Back Materials

Compilation of References

About the Contributors

Index

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