This article proposes a semi-interactive system for visual data exploration using an iterative clustering that combines an automatic approach with an interactive one. We propose a framework to improve the interactivity between the user and the data analysis process, allowing him or her to participate actively in the iterative clustering tasks using a two-dimensional projection. Defining a cluster by its seed (center) and its limit, the proposed approach allows the user to modify the automated values or to define new seeds and the associated cluster limit himself or herself. The user can perform the clustering according to his or her visual perception manually and can also choose to let the automated approach find optimal seeds and then interact with the process to iterate the clustering process according to his or her visual perception and domain knowledge. Most of the evaluation criteria for clustering evaluate the complete clustering and not each cluster separately. In this article, we propose to adapt evaluation criteria to single clusters, allowing the users to evaluate their own clusters and perform the clustering iteratively until satisfaction. To evaluate our proposed approach, we conduct a user evaluation, where the users are asked to perform clustering interactively according to their visual perception and with the semi-interactive one. We also compare the obtained results with those of automated clustering. The quantitative results have shown that the cooperative approach can improve the clustering results in terms of accuracy.
Electrical power networks are heavily monitored systems, requiring operators to perform intricate information synthesis before understanding the underlying network state. Our study aims at helping this synthesis step by automatically creating features from the sensor data. We propose a supervised feature extraction approach using a grammar-guided evolution, which outputs interpretable and dimensionally consistent features. Operations restrictions on dimensions are introduced in the learning process through context-free grammars. They ensure coherence with physical laws, dimensional-consistency, and also introduce technical expertise in the created features. We compare our approach to other state-of-theart feature extraction methods on a real dataset taken from the French electrical network sensors.
Abstract. We propose a novel clustering-based outlier detection approach for data streams. To deal with the data streams, we propose splitting the data into several windows. In each window, the data is divided into subspaces. First, a clustering algorithm is applied on one subspace. Based on the existing relations between the different subspaces, the obtained clusters can represent partitions on another subspace. Then the same clustering algorithm is applied on each partition separately in this second subspace. The process can be iterated on n subspaces. We perform tests on firewall logs data sets, we choose to test our approach with two subspaces and to visualize the results with neighborhood graphs in each window. A comparison is provided between the obtained results and the MCOD algorithm results. We can identify visually the outliers events and observe the evolution of the stream.
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