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- 1Anomaly Detection
- 1Anomaly detection
- 1Approximation algorithms
- 1Aftergood, Olivia SR
- 1Al Dallal, Ahmed
- 1Behsaz, Babak
- 1Berube, Paul N. J.
- 1Cheng, Hao
- 1Falcao, Alexandre
- 8Department of Computing Science
- 5Department of Electrical and Computer Engineering
- 3Department of Civil and Environmental Engineering
- 1Department of Mathematical and Statistical Sciences
- 1Department of Mechanical Engineering
- 1Department of Pharmacology
- 2Sander, Joerg (Computing Science)
- 1Ardakanian, Omid (Computing Science)
- 1Cockburn, Bruce (Department of Electrical and Computer Engineering)
- 1Deutsch, Clayton (Civil and Environmental Engineering)
- 1Dick, Scott (Department of Electrical and Computer Engineering)
- 1Flannigan, Mike D (Renewable Resources)
Recent proliferation of low-cost and lightweight GPS tracking devices led to a large increase in the amounts of collected mobility data. The rapidly emerging field of location-based services requires accurate and informative knowledge mining from these large quantities of data. One such mobility...
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...
The objective of this thesis is to develop, implement and verify a theoretical framework based upon aggregation and mathematical programming for solving the long-term open pit production planning problem. The goal is to closely estimate the maximum net present value of the operation by providing...
An Adaptive and Efficient Clustering-based Approach for Content Based Image Retrieval in Image Databases
An Adaptive and Efficient Clustering-based Approach for Content Based Image Retrieval in Image DatabasesDownload
Technical report TR01-03. In this paper, we present a new Content-Based Image Retrieval (CBIR) approach which is based on cluster analysis. A distinguishing aspect of CBIR is that it relies on a visual content representation (metadata) of the images. In order to produce such metadata, we propose...
An industrial construction enterprise operating in the City of Edmonton wants to improve its bidding strategies that are currently plagued with uncertainty, lack of information and historical price variability. The present research studies a compilation of documents obtained from company archives...
In this thesis, we present some approximation algorithms for the following clustering problems: Minimum Sum of Radii (MSR), Minimum Sum of Diameters (MSD), and Unsplittable Capacitated Facility Location. Given a metric (V, d) and an integer k, we consider the problem of partitioning the points...
In this thesis, we consider two closely related clustering problems, Min Sum k-Clustering (MSkC) and Balanced k-Median (BkM). In Min Sum k-clustering, one is given a graph and a parameter k, and has to partition the vertices in the graph into k clusters to minimize the sum of pairwise distances...
Due to its wide application in various fields, clustering, as a fundamental unsupervised learning problem, has been intensively investigated over the past few decades. Unfortunately, standard clustering formulations are known to be computationally intractable. Although many convex relaxations of...
Anomaly detection in spatial time series is a challenging problem with numerous potential applications. A comprehensive anomaly detection approach not only should be able to detect and identify the emerging anomalies, but it also has to characterize the essence of these anomalies by visualizing...
Survival data is mostly analyzed using Cox proportional hazards model to identify factors associated with survival time of patients. However recently random survival forest (RSF), a non-parametric method for ensemble estimation constructed by bagging of classification trees for survival data, is...