Data Analysis using Multidimensional Modeling, Statistical Analysis and Data Mining on Agriculture Parameters

TL;DR: This paper focuses on optimizing effect of weather on agriculture using various techniques like Correlation Analysis, multidimensional modeling, k-means, ANN, SVM, KG classification, PAM, CLARA, DBSCAN etc.

TL;DR: An approach to publish statistical data from public repositories by using Semantic Web standards published by the W3C, such as RDF and SPARQL, in order to facilitate the analysis of multidimensional models.

TL;DR: In this article, an agglomerative hierarchical clustering algorithm is used to determine compatibility groups of different fruits and vegetables that can be stored and transported together based upon their requirements for temperature, relative humidity, odour and ethylene production.

TL;DR: In this paper, a data mining study of agricultural meteorological patterns collected from meteorological centre of Bengaluru district is described, where K Means and Hierarchical clustering techniques are used to extract patterns like minimum (15 to 17°C), maximum (28 to 29°C) air temperature, relative humidity (79 to 96%) in the interim of morning hours, rainfall (0 mm) and pan evaporation (5.22 to 7.2 mm) which gives great significance to predict probable result.

TL;DR: This paper proposes a complete knowledge discovery in databases (KDD) model that uses a new method derived from association rules programming, and is based on two concepts: decision correlation rules and contingency vectors.

TL;DR: This paper analyses the data of rural labor, arable land area and the gross output value of agriculture about 30 cities of china based on the decision tree, and adopts clustering analysis method to discretize continuous data during the process of data miming in order to subjectivity comparing to the traditional classification methods.

TL;DR: Wang et al. as mentioned in this paper analyzed the data of rural labor, arable land area and gross output value of agriculture about 30 cities of China based on the decision tree, and adopted clustering analysis method in separating continuous data during the process of data miming in order to avoid subjectivity comparing to the traditional classification methods.

TL;DR: This paper uses multivariate split analysis to filter diagnosis data for symptoms having real impact on faults and their repair measures, thus detecting diagnosis functions which have to be updated as they contain irrelevant or erroneous observations and/or repair measurements.