title: Applying clustering analysis for discovering time series heterogeneity using Saint Petersburg morbidity rate as an illustration
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir M.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Staroverova, Kseniya Yu.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2017-02-09T09:17:33Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2017-02-09T09:17:33Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2016-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Bure V. M., Staroverova K. U. Applying clustering analysis for discovering time series heterogeneity using Saint Petersburg morbidity rate as an illustration. Vestnik of Saint Petersburg University. Series 10. Applied mathematics. Computer science. Control processes, 2016, issue 4, pp. 44–50.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu10.2016.404
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/5995
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: One of the machine learning approaches for unsupervised learning is clustering. Clustering has
  the task of exploring the structure of data with the aim of assigning a set of objects in such a
  way that objects belonging to the same group are more similar to each other than the objects
  drawn from different groups. Determining the number of clusters in a data set, searching for
  stable clusters, selection of dissimilarity measure and algorithm are significant tasks of cluster
  analysis. Multidimentional clustering is often used when an object is characterized by a vector. A
  dissimilarity measure or distance is selected with respect to the purpose and features of a certain
  task. But there are also such fields as economics, geology, medicine, sociology that are often
  presented by time series. Time series are random processes but not a random vector. That is why
  it is important to construct such a similarity (or dissimilarity) measure which would take into
  consideration that data are time–dependent. The research of morbidity rate of Saint Petersburg
  from 1999 to 2014 years and clustering of 18 districts are conducted. Several different similarity
  measures are used for clustering. Besides, an interesting aspect is clustering of multidimentional
  time series. There are two approaches. The first concept is to split multidimentional time series
  into several univariate time series, whilst the second one is to consider it as a whole unit that
  preserves the influence of data interdependence. Research is made with application of TSclust,
  tseries packages in R and missed algorithms are realised there. As a result of clustering of
  Saint Petersburg districts applying several similarity measures three stable clusters are found
  out but seven districts do not belong to any cluster. Refs 10. Figs 2.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of Saint Petersburg University. Series 10. Applied Mathematics. Computer Science. Control Processes;Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: cluster analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: clustering
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: time series similarity measure
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: stable clusters
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Applying clustering analysis for discovering time series heterogeneity using Saint Petersburg morbidity rate as an illustration
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 04-Bure.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/5995/1/04-Bure.pdf
resources.format: Adobe PDF
resources.size: 633,29 kB