Trend Analysis Using Agglomerative Hierarchical Clustering Approach for Time Series Big Data

• Published in: Advances in Artificial Intelligence and Applied Cognitive Computing pp. 869 - 876
• Main Authors: Subbulakshmi, P., Vimal, S., Kaliappan, M., Robinson, Y. Harold, Kim, Mucheol
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• Series: Transactions on Computational Science and Computational Intelligence
• Subjects: Agglomerative hierarchical clustering; Big data; Paradigmatic time series; Trend analysis
• ISBN: 9783030702953; 3030702952
• ISSN: 2569-7072; 2569-7080
• DOI: 10.1007/978-3-030-70296-0_67

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 (DTW) is identified that calculates 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.