An empirical study of inter-concept similarities in multimedia ontologies

Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies

Integration of geoelectric and geochemical data using Self-Organizing Maps (SOM) to characterize a landfill

Leachates from garbage dumps can significantly compromise their surrounding area. Even if the distance between these and the populated areas could be considerable, the risk of affecting the aquifers for public use is imminent in most cases. For this reason, the delimitation and monitoring of the leachate plume are of significant importance. Geoelectric data (resistivity and IP), and surface methane measurements, are integrated and classified using an unsupervised Neural Network to identify possible risk zones in areas surrounding a landfill. The Neural Network used is a Kohonen type, which generates; as a result, Self-Organizing Classification Maps or SOM (Self-Organizing Map). Two graphic outputs were obtained from the training performed in which groups of neurons that presented a similar behaviour were selected. Contour maps corresponding to the location of these groups and the individual variables were generated to compare the classification obtained and the different anomalies associated with each of these variables. Two of the groups resulting from the classification are related to typical values of liquids percolated in the landfill for the parameters evaluated individually. In this way, a precise delimitation of the affected areas in the studied landfill was obtained, integrating the input variables via SOMs. The location of the study area is not detailed for confidentiality reasons.Comment: 11 pages, 7 figure

DATA VISUALIZATION OF ASYMMETRIC DATA USING SAMMON MAPPING AND APPLICATIONS OF SELF-ORGANIZING MAPS

Data visualization can be used to detect hidden structures and patterns in data sets that are found in data mining applications. However, although efficient data visualization algorithms to handle data sets with asymmetric proximities have been proposed, we develop an improved algorithm in this dissertation. In the first part of the proposal, we develop a modified Sammon mapping approach that uses the upper triangular part and the lower triangular part of an asymmetric distance matrix simultaneously. Our proposed approach is applied to two asymmetric data sets: an American college selection data set, and a Canadian college selection data set which contains rank information. When compared to other approaches that are used in practice, our modified approach generates visual maps that have smaller distance errors and provide more reasonable representations of the data sets. In data visualization, self-organizing maps (SOM) have been used to cluster points. In the second part of the proposal, we assess the performance of several software implementations of SOM-based methods. Viscovery SOMine is found to be helpful in determining the number of clusters and recovering the cluster structure of data sets. A genocide and politicide data set is analyzed using Viscovery SOMine, followed by another analysis on the public and private college data sets with the goal to find out schools with best values

Feature extraction of spatial panel data with autoencoders for clustering the Brazilian agricultural diversity.

ABSTRACT - Brazilian agricultural production presents a high degree of spatial diversity, which challenges designing territorial public policies to promote sustainable development. This article proposes a new approach to cluster Brazilian municipalities according to their agricultural production. It combines a feature extraction mechanism using Deep Learning based on Autoencoders and clustering based on k-means and Self-Organizing Maps. We used the panel data from IBGE?s annual estimates of Brazilian agricultural production between 1999 and 2018. Different structures of simple stacked undercomplete autoencoders were analyzed, varying the number of layers and neurons in each of them, including the latent layer. We evaluated the asymmetric exponential linear loss function to cope with the sparse data. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the Self-Organizing Maps and the k-means algorithm presented a better result than the clustering of the raw data from the k-means, demonstrating the ability of simple stacked autoencoders to reduce the dimensionality and create a new space of features in their latent layer where the data can be analyzed and clustered. Although the general accuracy is low, the results are promising, considering that we can add new improvements to the Deep Clustering process.GEOINFO 2022