Title :
Modifying the Scale-free Clustering Method
Author :
Päivinen, N.S. ; Grönfors, T.K.
Author_Institution :
Dept. of Comput. Sci., Kuopio Univ.
Abstract :
The aim of this study is to computationally classify, without supervision, the data points of a dataset containing real-life measurements pre-classified into two classes. The classification is done using two methods: the k-means method as a reference, and a modified version of previously presented method using a minimum spanning tree with a scale-free structure. The construction of a scale-free minimum spanning tree (SFMST) and its usage in clustering are presented, and the results of the modified SFMST clustering method are compared with the results obtained using the k-means method
Keywords :
pattern classification; pattern clustering; tree data structures; trees (mathematics); unsupervised learning; dataset; k-means method; minimum spanning tree; pattern classification; scale-free clustering method; Classification tree analysis; Clustering algorithms; Clustering methods; Complex networks; Computer science; Graph theory; Measurement errors; Network topology; Spatial databases; Tree graphs;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7695-2504-0
DOI :
10.1109/CIMCA.2005.1631514
