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Full-Text Articles in Artificial Intelligence and Robotics
An Unsupervised Machine Learning Algorithm For Clustering Low Dimensional Data Points In Euclidean Grid Space, Josef Lazar
An Unsupervised Machine Learning Algorithm For Clustering Low Dimensional Data Points In Euclidean Grid Space, Josef Lazar
Senior Projects Spring 2024
Clustering algorithms provide a useful method for classifying data. The majority of well known clustering algorithms are designed to find globular clusters, however this is not always desirable. In this senior project I present a new clustering algorithm, GBCN (Grid Box Clustering with Noise), which applies a box grid to points in Euclidean space to identify areas of high point density. Points within the grid space that are in adjacent boxes are classified into the same cluster. Conversely, if a path from one point to another can only be completed by traversing an empty grid box, then they are classified …
Research Of Intrusion Alert Aggregation Based On Spatial And Temporal Density, Zhang Jing, Hengjun Wang, Junquan Li, Bin Yu
Research Of Intrusion Alert Aggregation Based On Spatial And Temporal Density, Zhang Jing, Hengjun Wang, Junquan Li, Bin Yu
Journal of System Simulation
Abstract: Distributed Intrusion Detection System has created the problem to investigate a mass of duplicate alerts and high false positive rate in practical applications. Based on DBSCAN, density based spatial and temporal clustering of applications with noise (DBS&TCAN) algorithm was proposed by introducing temporal density. The approach aggregated partial alerts based on spatial density, and merges partial aggregation on the basis of temporal density. The effectiveness of the algorithm was demonstrated by the intrusion detection evaluation dataset. The comparative experiments and analysis show that the algorithm is effective in alert aggregation and gives better results in terms of real time …
Detecting Metagame Shifts In League Of Legends Using Unsupervised Learning, Dustin P. Peabody
Detecting Metagame Shifts In League Of Legends Using Unsupervised Learning, Dustin P. Peabody
University of New Orleans Theses and Dissertations
Over the many years since their inception, the complexity of video games has risen considerably. With this increase in complexity comes an increase in the number of possible choices for players and increased difficultly for developers who try to balance the effectiveness of these choices. In this thesis we demonstrate that unsupervised learning can give game developers extra insight into their own games, providing them with a tool that can potentially alert them to problems faster than they would otherwise be able to find. Specifically, we use DBSCAN to look at League of Legends and the metagame players have formed …