Data Mining: 15th Australasian Conference

This book constitutes the refereed proceedings of the 15th Australasian Conference on Data Mining, AusDM 2017, held in Melbourne, VIC, Australia, in August 2017.

The 17 revised full papers presented together with 11 research track papers and 6 application track papers were carefully reviewed and selected from 31 submissions. The papers are organized in topical sections on clustering and classification; big data; time series; outlier detection and applications; social media and applications.

Table of Contents

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