data mining techniques and applications


We propose the ap-plication of approximate reasoning tools as linguistic terms and fuzzy resemblance relations (less restrictive than sim-ilarity relations) and we discuss the obtained results from domain experts point of view.Evaluating and predicting the effectiveness of farmland consolidation on improving agricultural productivity in ChinaColor is an important feature that dictates the quality and consumer preferences of many fresh fruits and vegetables.

Furthermore, decision forests are more memory-intensive and less knowledge extractable than a single decision tree. Hence, in this thesis we propose several novel algorithms for improving accuracy of decision trees and decision forests, then propose a technique to reduce the size of decision forests while retaining or increasing the ensemble accuracy, and finally propose a framework for effective knowledge discovery from decision forests. The Central Queensland region has been subjected to several EWEs since 1999, yet the impacts of these events on local food production chains have not been examined in detail. In this tutorial, we will discuss the applications and the trend of data mining.The financial data in banking and financial industry is generally reliable and of high quality which facilitates systematic data analysis and data mining. Farmer has to face many troubles like lack of know-how, Manures, fertilizers and Agriculture marketing etc. There is a need to introduce computational means to study and predict optimal climatic factors for improved crop growth and yield. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. The file will be sent to your email address. If interested in research can workout together Applying Data Mining Techniques in the Field of Agriculture and Allied SciencesThis paper presents a novel learning framework to generate fine-grained temporal cultivation knowledge from large climatic sensor data. To better utilize data mining techniques the data in health care industry should properly collected, store and maintain. The exact prediction of crop yield helps formers to develop a suitable cultivation plan, crop health monitoring system, management of crop yield efficiently and also to establish the business strategy in order to decrease economic losses. It deals with the prediction of the wheat crop yielding using data mining techniques. Here is the list of areas in which data mining technology may be applied for intrusion detection −Privacy protection and information security in data mining.Data mining in telecommunication industry helps in identifying the telecommunication patterns, catch fraudulent activities, make better use of resource, and improve quality of service.

Proposed model is beneficial for increase in agricultural production and for cost control of Agro-products.Multidisciplinary Model for Smart Agriculture using Internet-of-Things (IoT), Sensors, Cloud-Computing, Mobile-Computing & Big-Data AnalysisThe “Digital Revolution” has blessed the human civilization with enormous amount of “DATA”. Crop models and decision tools are increasingly The most of existing algorithms for crop yield prediction utilizes the existing data mining (DM) techniques for forecasting. D. Ramesh et al. As such, an efficient way to evaluate the effectiveness of FC on All this information creates a set of data which can be used for learning ways of classifying future production yields because new sensor data are available Assessing the Impacts of Extreme Weather Events on Selected Fruit Production in Central Queensland, Australia: A Relational Analysis Using Flood Data... Data mining or data discovery is that the system aided technique of excavating through and investigating immense sets of information so extorting which means of the tidings. application of state-of-art GPS technology in connection with This section describes some of the trends in data mining that reflect the pursuit of these challenges. FC at a national scale. But it depends on the cleanliness of the healthcare data. Abstract Data mining is a process which finds useful patterns from large amount of data. Please read our short guide Results allowed the proposal of a new method for establishing the PG requirement to increase Ca²⁺ saturation to 60% in the effective cation exchange capacity (ECEC) at the 20-to 40-cm soil layer when this is lower than 54% using the following equation: PG (Mg ha–1) = (0.6 × ECEC – exchangeable Ca²⁺ content in cmolc dm–3) × 6.4. The data mining is a cost-effective and efficient solution compared to other statistical data applications. thus helping them to achieve better profits. The farmer community will be able to get the knowledge about future market price of agriculture commodity and accordingly plan for crop cultivation, crop production and other farm management activities. Some of the trends observed are smart farming, digital agriculture and Big Data Analytics which provide useful information regarding various crop yields influencing factors and predicting the accurate amounts of crop yield. Here is the list of areas where data mining is widely used − 1. Vegetation Index (NDVI) as samples to evaluate the effectiveness of FC. AgroCloud storage is used to store the details of farmers, periodic soil properties of farmlands, agro-vendors and agro-marketing agencies, Agro e-governance schemes and current environmental conditions. A phenomenal feature termed as ‘Predictability’ helps in predicting and portraying related data to the user according to their needs. used in an agricultural field to improve production efficiency.

Need help? These included maize (Zea mays L.), soybean [Glycine max (L.) Merr.

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