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Table of contents
1 Introduction
1.1 Context and Problematic
1.2 Positioning, objectives and case study of the thesis
1.3 Organization of the dissertation
2 Applicative context: Predictive maintenance to maximize rolling stock availability
2.1 Introduction
2.2 Data Mining: Denition and Process Overview
2.3 Railway Context
2.3.1 Existing Maintenance Policies
2.3.2 Data mining applied to the railway domain: A survey
2.4 Applicative context of the thesis: TrainTracer
2.4.1 TrainTracer Data
2.4.2 Raw data with challenging constraints
2.4.3 Cleaning bursts
2.5 Positioning our work
2.5.1 Approach 1: Association Analysis
2.5.2 Approach 2: Classication
3 Detecting pairwise co-occurrences using hypothesis testing-based ap-proaches: Null models and T-Patterns algorithm
3.1 Introduction
3.2 Association analysis
3.2.1 Introduction
3.2.2 Association Rule Discovery: Basic notations, Initial problem
3.3 Null models
3.3.1 Formalism
3.3.2 Co-occurrence scores
3.3.3 Randomizing data: Null models
3.3.4 Calculating p-values
3.3.5 Proposed Methodology: Double Null Models
3.4 T-Patterns algorithm
3.5 Deriving rules from discovered co-occurrences
3.5.1 Interestingness measures in data mining
3.5.2 Objective interestingness measures
3.5.3 Subjective Interestingness measures
3.6 Experiments on Synthetic Data
3.6.1 Generation Protocol
3.6.2 Experiments
3.7 Experiments on Real Data
3.8 Conclusion
4 Weighted Episode Rule Mining Between Infrequent Events
4.1 Introduction
4.2 Episode rule Mining in Sequences
4.2.1 Notations and Terminology
4.2.2 Literature review
4.3 Weighted Association Rule Mining: Relevant Literature
4.4 The Weighted Association Rule Mining Problem
4.5 Adapting the WARM problem for temporal sequences
4.5.1 Preliminary denitions
4.5.2 WINEPI algorithm
4.5.3 Weighted WINEPI algorithm
4.5.4 Calculating weights using Valency Model
4.5.5 Adapting Weighted WINEPI to include infrequent events
4.5.6 Adapting Weighted WINEPI to focus on target events: Oriented Weighted WINEPI
4.5.7 Experiments on synthetic data
4.5.8 Experiments on real data
4.6 Conclusion
5 Pattern recognition approaches for predicting target events
5.1 Pattern Recognition
5.1.1 Introduction
5.1.2 Principle
5.1.3 Preprocessing of data
5.1.4 Learning and classication
5.2 Supervised Learning Approaches
5.2.1 K-Nearest Neighbours Classier
5.2.2 Naive Bayes
5.2.3 Support Vector Machines
5.2.4 Articial Neural Networks
5.3 Transforming data sequence into a labelled observation matrix
5.4 Hypothesis testing: choosing the most signicant attributes
5.5 Experimental Results
5.5.1 Choice of performance measures
5.5.2 Choice of scanning window w
5.5.3 Performance of algorithms
5.6 Conclusion
6 Conclusion and Perspectives
6.1 Conclusion
6.2 Future Research Directions
