I would like to cluster uni-variate daily time series so that an emphasis is put on sudden drops in time series. Series that contain such uncommon drops should be in one cluster (drops should influence the distance to other series), whereas series that don't have such nosedives are in another cluster. Each series consists of 96 values (a 15-minutes aggregation interval).
Below you can see five exemplary series before clustering:
And here, after - series have been clustered with k-means with euclidean distance as a similarity measure:
In this specific case I would like to obtain a clustering that yields the 1:4 grouping, in which the red series (see picture 1) is in one cluster (its distance to "normal" series is big) and four other series are in the second cluster.
What is a suitable distance metric to be used in order to achieve the desired result? Should series be represented in other way, for example as a feature vector? If so, what is a statistical property that accounts for such abrupt changes?