A huge amount of data is produced in our days in the agriculture sector. Due to the huge amount of this datasets it is necessary to use data mining techniques in order to comprehend the data and extract useful information. In our work we apply three different data mining techniques to data about Olea europaea var. media oblonga from the island of Thassos, at the northern part of Greece. The dat...

In today’s world, gigantic amount of data is available in science, industry, business and many other areas. This data can provide valuable information which can be used by management for making important decisions. But problem is that how can find valuable information. The answer is data mining. Data Mining is popular topic among researchers. There is lot of work that cannot be explored till no...

Data mining has been used very frequently to extract hidden information from large databases. This paper suggests the use of decision trees for continuously extracting the clinical reasoning in the form of medical expert’s actions that is inherent in large number of EMRs (Electronic Medical records). In this way the extracted data could be used to teach students of oral medicine a number of ord...

Data mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. This study is to build the Graduates Employment Model using classification task in data mining, and to compare several of data-mining approaches such as Bayesian method and the Tree method. The Bayesian method includes 5 algorithms, including AODE, BayesNet, HNB, NaviveBayes, WAODE. The...

This research work deals with efficient data mining procedure for predicting the diabetes from medical records of patients. Diabetes is a very common disease these days in all populations and in all age groups. Diabetes contributes to heart disease, increases the risks of developing kidney disease, nerve damage, blood vessel damage and blindness. So mining the diabetes data in efficient manner ...

Novel data-mining tasks in e-science involve mining of distributed, highly heterogeneous data and knowledge sources. However, standard data mining platforms, such as Weka and Orange, involve only their own data mining algorithms in the process of knowledge discovery from local data sources. In contrast, next generation data mining technologies should enable processing of distributed data source...