Applying machine learning and data mining methods in DM research is a key approach to utilizing large volumes of available diabetes-related data for extracting knowledge. The severe social impact of the specific disease renders DM one of the main priorities in medical science research, which inevitably generates huge amounts of data.
This paper mainly compares the data mining tools deals with the health care problems. The comparative study compares the accuracy level predicted by data mining. complete research work. Classification rules performed well in the classification of blood donors, whose accuracy rate reached 89.9%(7). 3. DATA MINING.
Data Mining is a task of extracting the vital decision making information from a collective of past records for future analysis or prediction. The information may be hidden and is not identifiable without the use of data mining. The classification is one data mining technique through which the future outcome or.
The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications.
Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends.it is an analytic process designed to explore data (usually large amounts of data.
Data Mining Task: Tan, Kumar and Steinbach define data mining in their book “introduction to data mining”, as the process of automatically discovering useful information in large data (7). repository Data mining techniques are deployed to scour large databases in order to find novel and useful patterns that might remain unknown.
Among the data mining techniques developed in recent years, the data mining methods are including generalization, characterization, classification, clustering, association, evolution, pattern matching, data visualization and meta-rule guided mining.
Applying data mining in education also known as educational data mining (EDM), which enables to better understand how students learn and identify how improve educational outcomes. Present paper is.