Trend analysis using agglomerative hierarchical clustering approach for time series big data

Road traffic accidents are a 'global tragedy' that generates unpredictable chunks of data having heterogeneity. To avoid this heterogeneous tragedy, we need to fraternize and categorize the datasets. This can be done with the help of clustering and association rule mining techniques. As the trend of accidents is increasing throughout the year, agglomerative hierarchical clustering approach is proposed for time series big data for trend analysis. This clustering approach segments the time sequence data into different clusters after normalizing the discrete time sequence data. Agglomerative hierarchical clustering takes the objects with similar properties and groups them together to form the group of clusters. The paradigmatic time sequence (PTS) data for each cluster with the help of dynamic time warping are identified that calculate the closest time sequence. The PTS analyzes various zone details and forms a cluster to report the data. This approach is more useful and optimal than the traditional statistical techniques.

Automated summary

To prevent the tragedy of road traffic accidents, it is necessary to organize and categorize datasets using clustering and association rule mining techniques. The proposed agglomerative hierarchical clustering approach is used for trend analysis of time series big data, which segments the data into clusters after normalization.