The STUDIA UNIVERSITATIS BABEŞ-BOLYAI issue article summary

The summary of the selected article appears at the bottom of the page. In order to get back to the contents of the issue this article belongs to you have to access the link from the title. In order to see all the articles of the archive which have as author/co-author one of the authors mentioned below, you have to access the link from the author's name.

 
       
         
    STUDIA INFORMATICA - Issue no. 1 / 2014  
         
  Article:   MULTIOBJECTIVE APPROACH OF MULTI-DIMENSIONAL TIME SERIES CLUSTERING.

Authors:  .
 
       
         
  Abstract:   The multidimensional time series are a generalization of the single time series and are more difficult to cluster due to the higher number of parameters used to characterize a data instance. In this work we formulate the multidimensional time series clustering problem as a multi-objective problem and implement several distance measures in the k-means clustering algorithm in order to see the effect of the similarity measure in the clustering process. All the measures are geometrical distances. We used four data sets in order to validate the results. The Euclidean distance which is the most used one does not seem to be the most adequate measure in multidimensional clustering.

2010 Mathematics Subject Classi fication. 68P15, 68T05.1998 CR Categories and Descriptors. I.2.6[Computing Methodologies]: Arti ficial Intelligence - Learning; I.2.8[Computing Methodologies]: Problem Solving, Control Methods, and Search - Heuristic methods.

Key words and phrases. Bioinformatics, Dynamic clustering.
 
         
     
         
         
      Back to previous page