Data Mining for Service

Virtually all nontrivial and modern service related problems and systems involve data volumes and types that clearly fall into what is presently meant as “big data”, that is, are huge, heterogeneous, complex, distributed, etc.

Data mining is a series of processes which include collecting and accumulating data, modeling phenomena, and discovering new information, and it is one of the most important steps to scientific analysis of the processes of services.

Data mining application in services requires a thorough understanding of the characteristics of each service and knowledge of the compatibility of data mining technology within each particular service, rather than knowledge only in calculation speed and prediction accuracy. Varied examples of services provided in this book will help readers understand the relation between services and data mining technology. This book is intended to stimulate interest among researchers and practitioners in the relation between data mining technology and its application to other fields.

Table of Contents

Part I Fundamental Technologies Supporting Service Science
Chapter 1 Data Mining for Service
Chapter 2 Feature Selection Over Distributed Data Streams
Chapter 3 Learning Hidden Markov Models Using Probabilistic Matrix Factorization
Chapter 4 Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Terms
Chapter 5 Panel Data Analysis Via Variable Selection and Subject Clustering

Part II Knowledge Discovery from Text
Chapter 6 A Weighted Density-Based Approach for Identifying Standardized Items that are Significantly Related to the Biological Literature
Chapter 7 Nonnegative Tensor Factorization of Biomedical Literature for Analysis of Genomic Data
Chapter 8 Text Mining of Business-Oriented Conversations at a Call Center

Part III Approach for New Services in Social Media
Chapter 9 Scam Detection in Twitter
Chapter 10 A Matrix Factorization Framework for Jointly Analyzing Multiple Nonnegative Data Sources
Chapter 11 Recommendation Systems for Web 2.0 Marketing

Part IV Data Mining Spreading into Various Service Fields
Chapter 12 Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset
Chapter 13 Change Detection from Heterogeneous Data Sources
Chapter 14 Interesting Subset Discovery and Its Application on Service Processes
Chapter 15 Text Document Cluster Analysis Through Visualization of 3D Projections