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- 1Anomaly Detection
- 1Anomaly detection
- 1Attribute-value pairs
- 1Clustering parameter
- 1Co-location pattern
Density-based clustering methods extract high density clusters which are separated by regions of lower density. HDBSCAN* is an existing algorithm for producing a density-based cluster hierarchy. To obtain clusters from this hierarchy it includes an instance of FOSC(Framework for Optimal Selection...
Many clustering techniques require parameter settings and depending on an algorithms sensitivity to the parameter, the choice of the parameter value can be very important. Several approaches have been proposed to find the “best” value of the clustering parameter for the different unsupervised...
Identifying the peptide sequence from a mass spectrum is done either by database search or De novo peptide sequencing. This thesis focuses on identification of peptides by using database search, which is a process where an MS/MS spectrum is searched against an entire database of spectra...
Identifying spatial patterns of collisions is critical for improving the efficiency and effectiveness of traffic enforcement deployment and road safety. Recently, many studies have centred on finding locations with high collision concentration, so-called hotspots. However, most of them only focus...
The web contains a large volume of tables that provide structured information about entities and relationships. This data may be used as a source for exploratory searches and to gather information about desired entities. This thesis focuses on one particular exploratory search where given a query...
We address the problem of finding ‘surprising’ patterns of variable length in sequence data, where a surprising pattern is defined as a subsequence of a longer sequence, whose observed frequency is statistically significant with respect to a given distribution.Finding statistically significant...
We propose a two-phase method, called Multivariate Association Discovery (MAD), to mine temporal associations in multiple event sequences. It is assumed that a set of event sequences has been collected from an application, where each event has an id and an occurrence time. The goal is to detect...
Online trajectory prediction is central to the function of air traffic control of improving the flow of air traffic and preventing collisions, particularly considering the ever-increasing number of air travellers. In this thesis, we propose an approach to predict the mid-flight trajectory of an...
Cluster analysis plays a very important role for understanding various phenomena about data without any prior knowledge. However, hierarchical clustering algorithms, which are widely used for its representation of data, are computationally expensive. Recently large datasets are prevalent in many...
Spatial interaction pattern mining is the process of discovering patterns that occur due to the interaction of Boolean features from a spatial domain. A positive interaction of a subset of features generates a co-location pattern, whereas a negative interaction of a subset of features generates a...