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Data Mining

Overview of attention for book
Cover of 'Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Similarity Majority Under-Sampling Technique for Easing Imbalanced Classification Problem
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    Chapter 2 Rank Forest: Systematic Attribute Sub-spacing in Decision Forest
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    Chapter 3 Performance Evaluation of a Distributed Clustering Approach for Spatial Datasets
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    Chapter 4 Patched Completed Local Binary Pattern is an Effective Method for Neuroblastoma Histological Image Classification
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    Chapter 5 An Improved Naive Bayes Classifier-Based Noise Detection Technique for Classifying User Phone Call Behavior
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    Chapter 6 A Two-Sample Kolmogorov-Smirnov-Like Test for Big Data
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    Chapter 7 Exploiting Redundancy, Recurrency and Parallelism: How to Link Millions of Addresses with Ten Lines of Code in Ten Minutes
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    Chapter 8 SD-HOC: Seasonal Decomposition Algorithm for Mining Lagged Time Series
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    Chapter 9 An Incremental Anytime Algorithm for Mining T-Patterns from Event Streams
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    Chapter 10 Detection of Outlier Behaviour Amongst Health/Medical Providers Servicing TAC Clients
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    Chapter 11 Distributed Detection of Zero-Day Network Traffic Flows
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    Chapter 12 False Data Injection Attacks in Healthcare
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    Chapter 13 Identifying Precursors to Frequency Fluctuation Events in Electrical Power Generation Data
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    Chapter 14 Collaborative Filtering in an Offline Setting Case Study: Indonesia Retail Business
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    Chapter 15 Malicious Behaviour Analysis on Twitter Through the Lens of User Interest
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    Chapter 16 Meta-Heuristic Multi-objective Community Detection Based on Users’ Attributes
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    Chapter 17 A Semi-supervised Hidden Markov Topic Model Based on Prior Knowledge
Attention for Chapter 17: A Semi-supervised Hidden Markov Topic Model Based on Prior Knowledge
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Chapter title
A Semi-supervised Hidden Markov Topic Model Based on Prior Knowledge
Chapter number 17
Book title
Data Mining
Published by
Springer, Singapore, April 2018
DOI 10.1007/978-981-13-0292-3_17
Book ISBNs
978-9-81-130291-6, 978-9-81-130292-3
Authors

Sattar Seifollahi, Massimo Piccardi, Ehsan Zare Borzeshi

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 50%
Student > Ph. D. Student 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Computer Science 1 50%

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