Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery

Abstract

In the framework of ecological or environmental assessments and management, detection, characterization and forecasting of the dynamics of environmental states are of paramount importance. These states should reflect general patterns of change, recurrent or occasional events, long-lasting or short or extreme events which contribute to explain the structure and the function of the ecosystem. To identify such states, many scientific consortiums promote the implementation of Integrated Observing Systems which generate increasing amount of complex multivariate/multisource/multiscale datasets. Extracting the most relevant ecological information from such complex datasets requires the implementation of Machine Learning-based processing tools. In this context, we proposed a divisive spectral clustering architecture—the Multi-level Spectral Clustering (M-SC) which is, in this paper, extended with a no-cut criteria. This method is developed to perform detection events for data with a complex shape and high local connexity. While the M-SC method was firstly developed and implemented for a given specific case study, we proposed here to compare our new M-SC method with several existing direct and hierarchical clustering approaches. The clustering performance is assessed from different datasets with hard shapes to segment. Spectral methods are most efficient discovering all spatial patterns. For the segmentation of time series, hierarchical methods better isolated event patterns. The new M-SC algorithm, which combines hierarchical and spectral approaches, give promise results in the segmentation of both spatial UCI databases and marine time series compared to other approaches. The ability of our M-SC method to deal with many kinds of datasets allows a large comparability of results if applies within a broad Integrated Observing Systems. Beyond scientific knowledge improvements, this comparability is crucial for decision-making about environmental management.

MAREL Carnot data and metadata from Coriolis Data Center

“…For instance, researchers may now assess the effectiveness of new and old machine learning algorithms in understanding the dynamics and forecasting harmful algal blooms. Thus, a comparative study of clustering approaches applied to spatial or temporal pattern discovery gave promising results in the segmentation of both UCI databases and marine time series compared to other approaches (Grassi et al, 2020). Therefore, we may conclude that the MAREL Carnot dataset is beneficial not just for marine ecologists, but also for machine learning specialists and data scientists.…”

Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

“…Support vector machine (SVM), deep learning, and other clustering approaches are widely used for feature recognition [35][36][37]. Especially, SVM has the advantage of simple structure, fast learning speed, and wide applicability.…”