TIME SERIES CLUSTERING
Time
series clustering is to partition time series data into groups based on
seriability or distance so that time series in the same cluster are similar.
For
Time Series clustering with R the first time is
to work out an appropriate distance/similarity metric and then at the
second step , use existing clustering techniques such as k-means hierarchial
clustering , density based clustering or subspace clustering to find clustering
structures.
Clustering
is a solution for classifying enoromous data when there is not any early
knowledge about classes, with emerging new concepts like cloud computing and big data and their vast applications and
research works have been increased on unsupervised solutions like clustering to
extract knowledge from the data.
In
case of huge data set , using supervised classification solutions is almost
impossible , while clustering can solve this problem using un-supervised
approaches.
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