English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Time Series Clustering is an important subroutine in many higher-level datamining analyses, including data editing for classifiers, summarization, andoutlier detection. It is well known that for similarity search the superiorityof Dynamic Time Warping (DTW) over Euclidean distance gradually diminishes aswe consider ever larger datasets. However, as we shall show, the same is nottrue for clustering. Clustering time series under DTW remains a computationallyexpensive operation. In this work, we address this issue in two ways. Wepropose a novel pruning strategy that exploits both the upper and lower boundsto prune off a very large fraction of the expensive distance calculations. Thispruning strategy is admissible and gives us provably identical results to thebrute force algorithm, but is at least an order of magnitude faster. Fordatasets where even this level of speedup is inadequate, we show that we canuse a simple heuristic to order the unavoidable calculations in amost-useful-first ordering, thus casting the clustering into an anytimeframework. We demonstrate the utility of our ideas with both single andmultidimensional case studies in the domains of astronomy, speech physiology,medicine and entomology. In addition, we show the generality of our clusteringframework to other domains by efficiently obtaining semantically significantclusters in protein sequences using the Edit Distance, the discrete dataanalogue of DTW.

A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy