The application of a visual data mining framework to determine soil, climate and land-use relationships

In this research study, the methodology of action research dynamics and a case study was employed in constructing a visual data mining framework for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of a digital elevation model (DEM), soil and land use profiles that were juxtaposed with previously captured climatic data from fixed weather stations in Australia. In this pilot study, monthly rainfall profiles for a selected study area were used to identify areas of soil variability. The rainfall was sampled for the beginning (April) of the rainy season for the known ‘drought’ year 2002 for the South West of Western Australia. The components of the processing framework were a set of software tools such as ArcGis, QuantumGIS and the Microsoft Access database as part of the pre-processing layer. In addition, the GRASS software package was used for producing the map overlays. Evaluation was carried out using techniques of visual data mining to detect the patterns of soil types found for the cropping land use. This was supported by analysis using WEKA and Microsoft Excel for validation. The results suggest that agriculture in these areas of high soil variability need to be managed differently to the more consistent cropping areas. Although this processing framework was used to analyse soil and rainfall climate data pertaining to agriculture in Western Australia; it is easily applicable to other datasets of a similar attribution in different areas

Visual Discovery in Multivariate Binary Data

This paper presents the concept of Monotone Boolean Function Visual Analytics (MBFVA) and its application to the medical domain. The medical application is concerned with discovering breast cancer diagnostic rules (i) interactively with a radiologist, (ii) analytically with data mining algorithms, and (iii) visually. The coordinated visualization of these rules opens an opportunity to coordinate the rules, and to come up with rules that are meaningful for the expert in the field, and are confirmed with the database. This paper shows how to represent and visualize binary multivariate data in 2-D and 3-D. This representation preserves the structural relations that exist in multivariate data. It creates a new opportunity to guide the visual discovery of unknown patterns in the data. In particular, the structural representation allows us to convert a complex border between the patterns in multidimensional space into visual 2-D and 3-D forms. This decreases the information overload on the user. The visualization shows not only the border between classes, but also shows a location of the case of interest relative to the border between the patterns. A user does not need to see the thousands of previous cases that have been used to build a border between the patterns. If the abnormal case is deeply inside in the abnormal area, far away from the border between normal and abnormal patterns, then this shows that this case is very abnormal and needs immediate attention. The paper concludes with the outline of the scaling of the algorithm for the large data sets

Explorative Graph Visualization

Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verständnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstützt. Ziel dieser Arbeit ist es, einen Überblick über die Probleme dieser Visualisierungen zu geben und konkrete Lösungsansätze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingeführt, um den Nutzen der geführten Diskussion für die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization

Mining climate data for shire level wheat yield predictions in Western Australia

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations

VizAssist (un assistant utilisateur pour le choix et le paramétrage des méthodes de fouille visuelle de données)

Nous nous intéressons dans cette thèse au problème de l automatisation du processus de choix et de paramétrage des visualisations en fouille visuelle de données. Pour résoudre ce problème, nous avons développé un assistant utilisateur "VizAssist" dont l objectif principal est de guider les utilisateurs (experts ou novices) durant le processus d exploration et d analyse de leur ensemble de données. Nous illustrons, l approche sur laquelle s appuie VizAssit pour guider les utilisateurs dans le choix et le paramétrage des visualisations. VizAssist propose un processus en deux étapes. La première étape consiste à recueillir les objectifs annoncés par l utilisateur ainsi que la description de son jeu de données à visualiser, pour lui proposer un sous ensemble de visualisations candidates pour le représenter. Dans cette phase, VizAssist suggère différents appariements entre la base de données à visualiser et les visualisations qu il gère. La seconde étape permet d affiner les différents paramétrages suggérés par le système. Dans cette phase, VizAssist utilise un algorithme génétique interactif qui a pour apport de permettre aux utilisateurs d évaluer et d ajuster visuellement ces paramétrages. Nous présentons enfin les résultats de l évaluation utilisateur que nous avons réalisé ainsi que les apports de notre outil à accomplir quelques tâches de fouille de données.In this thesis, we deal with the problem of automating the process of choosing an appropriate visualization and its parameters in the context of visual data mining. To solve this problem, we developed a user assistant "VizAssist" which mainly assist users (experts and novices) during the process of exploration and analysis of their dataset. We illustrate the approach used by VizAssit to help users in the visualization selection and parameterization process. VizAssist proposes a process based on two steps. In the first step, VizAssist collects the user s objectives and the description of his dataset, and then proposes a subset of candidate visualizations to represent them. In this step, VizAssist suggests a different mapping between the database for representation and the set of visualizations it manages. The second step allows user to adjust the different mappings suggested by the system. In this step, VizAssist uses an interactive genetic algorithm to allow users to visually evaluate and adjust such mappings. We present finally the results that we have obtained during the user evaluation that we performed and the contributions of our tool to accomplish some tasks of data mining.TOURS-Bibl.électronique (372610011) / SudocSudocFranceF