A data-dependent dissimilarity measure: An effective alternative to distance measures SUNIL ARYAL 10.4225/03/5a2f0721de429 https://bridges.monash.edu/articles/thesis/A_data-dependent_dissimilarity_measure_An_effective_alternative_to_distance_measures/5687527 In data mining, the task-specific performances of conventional distance-based similarity measures vary significantly in different data distributions because they are data-independent and sensitive to units or scales of measurement. This thesis investigates a measure, where the similarity of two instances is determined by the distribution of data. It introduces a new (dis)similarity measure, which is data-dependent and robust to units and scales of measurement. The empirical evaluation conducted across a wide range of datasets shows that the new measure produces better or at least more consistent task-specific performance than widely-used distance-based measures, particularly in high-dimensional datasets. 2017-12-11 22:30:56 Similarity measure Distance measure Minkowski distance Data-dependent similarity measure Mp-dissimilarity Knowledge Representation and Machine Learning Pattern Recognition and Data Mining