Paper Info
Title
Two Different Methods for Initialization the I-k-Means Clustering of Time Series Data
Abstract
I-k-Means is a popular clustering algorithm for time series data transformed by a multiresolution dimensionality reduction method. In this paper, we compare two different methods for initialization the I-k-means clustering algorithm. The first method uses kd tree and the second applies cluster-feature tree (CF-tree) to determine initial centers. In both approaches of clustering, we employ a new method for time series dimensionality reduction, MP_C, which can be easily made a multi-resolution feature extraction technique. Our experiments show that both initialization methods yield almost the same clustering quality, however the running time of I-k-Means initialized by using CF tree is a bit higher than that of the I-k-means initialized by using kd-tree. Both of the clustering approaches perform better than classical k-Means and I-k-Means in terms of clustering quality and running time.
Year
DOI
Venue
2011
10.1109/KSE.2011.10
KSE
Keywords
Field
DocType
time series data,cf tree,i-k-means clustering algorithm,clustering approach,i-k-means clustering,initialization method,popular clustering algorithm,different method,different methods,time series dimensionality reduction,cluster-feature tree,clustering quality,kd tree,k means,k means clustering,feature extraction,clustering,time series
k-medians clustering,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Machine learning,Single-linkage clustering
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Nguyen Thanh Son1357.03
Duong Tuan Anh25823.06