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3.3 k-Means Clustering ......................... 70
3.4 Association Rule Learning ...................... 74
3.5 Sequence and Episode Mining . ................... 77
3.5.1 Sequence Mining . . . ................... 77
3.5.2 Episode Mining ....................... 78
3.5.3 Other Approaches . . . ................... 81
3.6 Quality of Resulting Models . . ................... 82
3.6.1 Measuring the Performance of a Classifier ......... 83
3.6.2 Cross-Validation ....................... 85
3.6.3 Occam’s Razor ........................ 88

Part II From Event Logs to Process Models
4 Getting the Data ............................. 95
4.1 Data Sources ............................. 95
4.2 Event Logs .............................. 98
4.3 XES ................................. 107
4.4 Flattening Reality into Event Logs ................. 114
5 Process Discovery: An Introduction ................... 125
5.1 Problem Statement .......................... 125
5.2 A Simple Algorithm for Process Discovery ............. 129
5.2.1 Basic Idea .......................... 129
5.2.2 Algorithm .......................... 133
5.2.3 Limitations of the α-Algorithm ............... 136
5.2.4 Taking the Transactional Life-Cycle into Account ..... 139
5.3 Rediscovering Process Models ................... 140
5.4 Challenges . ............................. 144
5.4.1 Representational Bias . ................... 145
5.4.2 Noise and Incompleteness .................. 147
5.4.3 Four Competing Quality Criteria .............. 150
5.4.4 Taking the Right 2-D Slice of a 3-D Reality ........ 153
6 Advanced Process Discovery Techniques ................ 157
6.1 Overview . .............................. 157
6.1.1 Characteristic 1: Representational Bias . . ......... 159
6.1.2 Characteristic 2: Ability to Deal with Noise ......... 160
6.1.3 Characteristic 3: Completeness Notion Assumed ...... 161
6.1.4 Characteristic 4: Approach Used .............. 161
6.2 Heuristic Mining ........................... 163
6.2.1 Causal Nets Revisited . ................... 163
6.2.2 Learning the Dependency Graph .............. 164
6.2.3 Learning Splits and Joins .................. 167
6.3 Genetic Process Mining ....................... 169
6.4 Region-Based Mining ........................ 173
6.4.1 Learning Transition Systems ................ 174
6.4.2 Process Discovery Using State-Based Regions ....... 177