clinical databases can be categorized as big data, include large quantities of information about patients and their medical conditions. analyzing the quantitative and qualitative clinical data in addition with discovering relationships among huge number of samples using data mining techniques could unveil hidden medical knowledge in terms of correlation and association of apparently independent...

due to simple operation and no needs for expensive infrastructure construction to convey water from a source to farm lands, groundwater becomes the most important sources of agricultural water supply in iran. however, the contamination of aquifers is a major concern in many countries, specifically in areas without effective groundwater protection and management. therefore, groundwater vulnerabi...

objective(s): this study addresses feature selection for breast cancer diagnosis. the present process uses a wrapper approach using ga-based on feature selection and ps-classifier. the results of experiment show that the proposed model is comparable to the other models on wisconsin breast cancer datasets. materials and methods: to evaluate effectiveness of proposed feature selection method, we ...

Decision support systems (DSSs) are prevalent information systems for decision making in many competitive business environments. In a DSS, decision making process is intimately related to some factors which determine the quality of information systems and their related products. Traditional approaches to data analysis usually cannot be implemented in sophisticated Companies, where managers ne...

Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It sho...

Abstract— In this paper, we included the ambitious task of formulating a general framework of data mining. We explained that the framework should fulfil. It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. We also discuss data mining languages and what they should support: this includes the design and implementation of data mi...

The process of historical data analysis through data mining has proven valuable for the industrial environment. There are many models available that describe the in-house process of data mining. However, many companies either do not have in-house skills or do not wish to invest in performing in-house data mining. This paper investigates the applicability of two well-established data mining proc...