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Information Technology

Computing & Information Technology

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B2B & B2C

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.

Data Mining for Service

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

SBD: DATA MINING FOR SERVICE

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Editorial Board.
Other Frontmatter.
Title Page.
Copyright Page.
Preface.
Contents.
1: Fundamental Technologies Supporting Service Science.
2: Data Mining for Service.
3: Feature Selection Over Distributed Data Streams.
4: Learning Hidden Markov Models Using Probabilistic Matrix Factorization.
5: Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Terms.
6: Panel Data Analysis Via Variable Selection and Subject Clustering.
7: Knowledge Discovery from Text.
8: A Weighted Density-Based Approach for Identifying Standardized Items that are Significantly Related to the Biological Literature.
9: Nonnegative Tensor Factorization of Biomedical Literature for Analysis of Genomic Data.
10: Text Mining of Business-Oriented Conversations at a Call Center.
11: Approach for New Services in Social Media.
12: Scam Detection in Twitter.
13: A Matrix Factorization Framework for Jointly Analyzing Multiple Nonnegative Data Sources.
14: Recommendation Systems forWeb 2.0 Marketing.
15: Data Mining Spreading into Various Service Fields.
16: Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset.
17: Change Detection from Heterogeneous Data Sources.
18: Interesting Subset Discovery and Its Application on Service Processes.
19: Text Document Cluster Analysis Through Visualization of 3D Projections.

Data Mining for Service.css-1p8rvzy{position:absolute;padding-left:8px;}

Data Mining for Service