25,817 research outputs found
Constrained Distance Based Clustering for Satellite Image Time-Series
International audienceThe advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and k-Means based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semi-supervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multi-spectral Landsat images non-uniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images non-uniformly sampled between 2016 and 2018
Uncovering Group Level Insights with Accordant Clustering
Clustering is a widely-used data mining tool, which aims to discover
partitions of similar items in data. We introduce a new clustering paradigm,
\emph{accordant clustering}, which enables the discovery of (predefined) group
level insights. Unlike previous clustering paradigms that aim to understand
relationships amongst the individual members, the goal of accordant clustering
is to uncover insights at the group level through the analysis of their
members. Group level insight can often support a call to action that cannot be
informed through previous clustering techniques. We propose the first accordant
clustering algorithm, and prove that it finds near-optimal solutions when data
possesses inherent cluster structure. The insights revealed by accordant
clusterings enabled experts in the field of medicine to isolate successful
treatments for a neurodegenerative disease, and those in finance to discover
patterns of unnecessary spending.Comment: accepted to SDM 2017 (oral
Dynamic Adaptation on Non-Stationary Visual Domains
Domain adaptation aims to learn models on a supervised source domain that
perform well on an unsupervised target. Prior work has examined domain
adaptation in the context of stationary domain shifts, i.e. static data sets.
However, with large-scale or dynamic data sources, data from a defined domain
is not usually available all at once. For instance, in a streaming data
scenario, dataset statistics effectively become a function of time. We
introduce a framework for adaptation over non-stationary distribution shifts
applicable to large-scale and streaming data scenarios. The model is adapted
sequentially over incoming unsupervised streaming data batches. This enables
improvements over several batches without the need for any additionally
annotated data. To demonstrate the effectiveness of our proposed framework, we
modify associative domain adaptation to work well on source and target data
batches with unequal class distributions. We apply our method to several
adaptation benchmark datasets for classification and show improved classifier
accuracy not only for the currently adapted batch, but also when applied on
future stream batches. Furthermore, we show the applicability of our
associative learning modifications to semantic segmentation, where we achieve
competitive results
- …