FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2916022325> ?p ?o ?g. }

• W2916022325 abstract "Clustering algorithms attempt the identification of distinct subgroups within heterogeneous data and are commonly utilised as an exploratory tool. The definition of a cluster is dependent on the relevant dataset and associated constraints; clustering methods seek to determine homogeneous subgroups that each correspond to a distinct set of characteristics. This thesis focuses on the development of spatial clustering algorithms and the methods are motivated by the complexities posed by spatio-temporal data. The examples in this thesis primarily come from spatial structures described in the context of traffic modelling and are based on occupancy observations recorded over time for an urban road network. Levels of occupancy indicate the extent of traffic congestion and the goal is to identify distinct regions of traffic congestion in the urban road network.Spatial clustering for spatio-temporal data is an increasingly important research problem and the challenges posed by such research problems often demand the development of bespoke clustering methods. Many existing clustering algorithms, with a focus on accommodating the underlying spatial structure, do not generate clusters that adequately represent differences in the temporal pattern across the network. This thesis is primarily concerned with developing nonparametric clustering algorithms that seek to identify spatially contiguous clusters and retain underlying temporal patterns. Broadly, this thesis introduces two clustering algorithms that are capable of accommodating spatial and temporal dependencies that are inherent to the dataset. The first is a functional distributional clustering algorithm that is implemented within an agglomerative hierarchical clustering framework as a two-stage process. The method is based on a measure of distance that utilises estimated cumulative distribution functions over the data and this unique distance is both functional and distributional. This notion of distance utilises the differences in densities to identify distinct clusters in the graph, rather than raw recorded observations.However, distinct characteristics may not necessarily be identified and distinguishable by a densities-based distance measure, as defined within the agglomerative hierarchical clustering framework. In this thesis, we also introduce a formal Bayesian clustering approach that enables the researcher to determine spatially contiguous clusters in a data-driven manner. This framework varies from the set of assumptions introduced by the functional distributional clustering algorithm. This flexible Bayesian model employs a binary dependent Chinese restaurant process (binDCRP) to place a prior over the geographical constraints posed by a graph-based network. The binDCRP is a special case of the distance dependent Chinese restaurant process that was first introduced by Blei and Frazier (2011); the binDCRP is modified to account for data that poses spatial constraints. The binDCRP seeks to cluster data such that adjacent or neighbouring regions in a spatial structure are more likely to belong to the same cluster. The binDCRP introduces a large number of singletons within the spatial structure and we modify the binDCRP to enable the researcher to restrict the number of clusters in the graph. It is also reasonable to assume that individual junctions within a cluster are spatially correlated to adjacent junctions, due to the nature of traffic and the spread of congestion. In order to fully account for spatial correlation within a cluster structure, the model utilises a type of the conditional auto-regressive (CAR) model. The model also accounts for temporal dependencies using a first order auto-regressive (AR-1) model. In this mean-based flexible Bayesian model, the data is assumed to follow a Gaussian distribution and we utilise Kronecker product identities within the definition of the spatio-temporal precision matrix to improve the computational efficiency. The model utilises a Metropolis within Gibbs sampler to fully explore all possible partition structures within the network and infer the relevant parameters of the spatio-temporal precision matrix. The flexible Bayesian method is also applicable to map-based spatial structures and we describe the model in this context as well.The developed Bayesian model is applied to a simulated spatio-temporal dataset that is composed of three distinct known clusters. The differences in the clusters are reflected by distinct mean values over time associated with spatial regions. The nature of this mean-based comparison differs from the functional distributional clustering approach that seeks to identify differences across the distribution. We demonstrate the ability of the Bayesian model to restrict the number of clusters using a simulated data structure with distinctly defined clusters. The sampler is also able to explore potential cluster structures in an efficient manner and this is demonstrated using a simulated spatio-temporal data structure. The performance of this model is illustrated by an application to a dataset over an urban road network that presents traffic as a process varying continuously across space and time. We also apply this model to an areal unit dataset composed of property prices over a period of time for the Avon county in England." @default.
• W2916022325 created "2019-03-02" @default.
• W2916022325 creator A5014375278 @default.
• W2916022325 date "2019-01-01" @default.
• W2916022325 modified "2023-09-26" @default.
• W2916022325 title "Nonparametric clustering for spatio-temporal data" @default.
• W2916022325 cites W14612285 @default.
• W2916022325 cites W1499718974 @default.
• W2916022325 cites W1518206781 @default.
• W2916022325 cites W1551893515 @default.
• W2916022325 cites W1576898078 @default.
• W2916022325 cites W1605548525 @default.
• W2916022325 cites W1660492679 @default.
• W2916022325 cites W195533127 @default.
• W2916022325 cites W1977958056 @default.
• W2916022325 cites W1983908352 @default.
• W2916022325 cites W1987315804 @default.
• W2916022325 cites W1987971958 @default.
• W2916022325 cites W1990368529 @default.
• W2916022325 cites W1997568940 @default.
• W2916022325 cites W1998378660 @default.
• W2916022325 cites W1998871699 @default.
• W2916022325 cites W2003390846 @default.
• W2916022325 cites W2004014822 @default.
• W2916022325 cites W2016381774 @default.
• W2916022325 cites W2021091247 @default.
• W2916022325 cites W2022237013 @default.
• W2916022325 cites W2026261408 @default.
• W2916022325 cites W2026820593 @default.
• W2916022325 cites W2033403400 @default.
• W2916022325 cites W2038885294 @default.
• W2916022325 cites W2039934205 @default.
• W2916022325 cites W2045656233 @default.
• W2916022325 cites W2049882758 @default.
• W2916022325 cites W2052198885 @default.
• W2916022325 cites W2053101680 @default.
• W2916022325 cites W2056760934 @default.
• W2916022325 cites W2061213247 @default.
• W2916022325 cites W2071949631 @default.
• W2916022325 cites W2076434863 @default.
• W2916022325 cites W2083875149 @default.
• W2916022325 cites W2086662759 @default.
• W2916022325 cites W2090634555 @default.
• W2916022325 cites W2093223772 @default.
• W2916022325 cites W2098731115 @default.
• W2916022325 cites W2102862543 @default.
• W2916022325 cites W2122740842 @default.
• W2916022325 cites W2125386307 @default.
• W2916022325 cites W2129116669 @default.
• W2916022325 cites W2131398657 @default.
• W2916022325 cites W2134179844 @default.
• W2916022325 cites W2138309709 @default.
• W2916022325 cites W2142590786 @default.
• W2916022325 cites W2144126515 @default.
• W2916022325 cites W2145641621 @default.
• W2916022325 cites W2148534890 @default.
• W2916022325 cites W2148943178 @default.
• W2916022325 cites W2152977846 @default.
• W2916022325 cites W2156619449 @default.
• W2916022325 cites W2165874743 @default.
• W2916022325 cites W2198206085 @default.
• W2916022325 cites W2326628520 @default.
• W2916022325 cites W2404659965 @default.
• W2916022325 cites W2483632691 @default.
• W2916022325 cites W2560430923 @default.
• W2916022325 cites W2594496282 @default.
• W2916022325 cites W2615915281 @default.
• W2916022325 cites W2620598837 @default.
• W2916022325 cites W2913066018 @default.
• W2916022325 cites W2999729612 @default.
• W2916022325 cites W3145506661 @default.
• W2916022325 cites W79918814 @default.
• W2916022325 hasPublicationYear "2019" @default.
• W2916022325 type Work @default.
• W2916022325 sameAs 2916022325 @default.
• W2916022325 citedByCount "0" @default.
• W2916022325 crossrefType "dissertation" @default.
• W2916022325 hasAuthorship W2916022325A5014375278 @default.
• W2916022325 hasConcept C124101348 @default.
• W2916022325 hasConcept C154945302 @default.
• W2916022325 hasConcept C166957645 @default.
• W2916022325 hasConcept C17212007 @default.
• W2916022325 hasConcept C193143536 @default.
• W2916022325 hasConcept C205649164 @default.
• W2916022325 hasConcept C2779343474 @default.
• W2916022325 hasConcept C27964816 @default.
• W2916022325 hasConcept C33704608 @default.
• W2916022325 hasConcept C41008148 @default.
• W2916022325 hasConcept C73555534 @default.
• W2916022325 hasConceptScore W2916022325C124101348 @default.
• W2916022325 hasConceptScore W2916022325C154945302 @default.
• W2916022325 hasConceptScore W2916022325C166957645 @default.
• W2916022325 hasConceptScore W2916022325C17212007 @default.
• W2916022325 hasConceptScore W2916022325C193143536 @default.
• W2916022325 hasConceptScore W2916022325C205649164 @default.
• W2916022325 hasConceptScore W2916022325C2779343474 @default.
• W2916022325 hasConceptScore W2916022325C27964816 @default.
• W2916022325 hasConceptScore W2916022325C33704608 @default.
• W2916022325 hasConceptScore W2916022325C41008148 @default.
• W2916022325 hasConceptScore W2916022325C73555534 @default.

• next