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These include managing routine, on-going linkage; storing and handling changing data; handling different linkage scenarios; accommodating ever increasing datasets.
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However, most clustering algorithms suffer from two drawbacks: they do not scale well with increasing dataset sizes and often require proper parametrization which is usually difficult to provide.
With increasing dataset sizes, current best practice of manually reviewing videos to classify the occurrence of driving behaviours, including those that are indicative of distraction, is becoming increasingly impractical.
Figure 5b shows that execution time increases nearly linearly with increasing dataset size.
The results have been analysed based on various sizes of datasets, better results have been achieved with increasing dataset size.
While the time required by Sachem/OrChem to answer all queries was roughly linear with increasing dataset size, Sachem/Lucy behaved more efficiently.
Hence, even with increasing dataset sizes, it is important to verify that the clustering method used performs no worse than complete linkage.
Although SLiMFinder runtimes do appear to increase exponentially with increasing dataset size, the slope of the curve is very shallow and none of the test datasets took more than an hour to run on a single 3.2GHz processor (Figure S2A).
It is easily scalable to increasing dataset number.
However, Sma3s annotation times were much lower and barely affected by increasing dataset sizes.
Deviations decreased with increasing dataset size, but were already close to the optimum at 1 mio reads.
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