Study Of Data Mining Techniques Research Articles

Data Mining Techniques for Early Diagnosis of Diabetes: A Comparative Study

Diabetes is a life-long condition that is well-known in the 21st century. Once known as a disease of the West, the rise of diabetes has been fed by a nutrition shift, rapid urbanization and increasingly sedentary lifestyles. In late 2019, a new public health concern was emerging (COVID-19), with a particular hazard concerning people living with diabetes. Medical institutes have been collecting data for years. We expect to achieve predictions for pathological complications, which hopefully will prevent the loss of lives and improve the quality of life using data mining processes. This work proposes a comparative study of data mining techniques for early diagnosis of diabetes. We use a publicly accessible data set containing 520 instances, each with 17 attributes. Naive Bayes, Neural Network, AdaBoost, k-Nearest Neighbors, Random Forest and Support Vector Machine methods have been tested. The results suggest that Neural Networks should be used for diabetes prediction. The proposed model presents an AUC of 98.3% and 98.1% accuracy, an F1-Score, Precision and Sensitivity of 98.4% and a Specificity of 97.5%.

Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey

In this study multiple linear regression, multilayer perceptron (MLP) regression, and support vector regression (SVR) are used to make multivariate tourism forecasting for Turkey. This paper is a comparative study of data mining techniques based on multivariate regression modelling with monthly data points to forecast tourism demand; it focuses on Turkey. Both MLP and SVR methods are widely employed in the variety forecasting problems. Most of the previous research on tourism forecasting used univariate time series or a limited number of variables with mostly yearly or quarterly, and rarely monthly frequencies. However, the application of data mining techniques for multivariate forecasting in the context of tourism demand has not been widely explored. This paper differs from earlier research in two ways: 1) it proposes multivariate regression modelling with monthly data points to forecast tourism demand; and 2) it focuses on Turkey by using a dataset with the most recently accumulated (between January 1996 and Dec 2013) 67 time series with respect to Turkey and its top 26 major tourism clients. Comparison of forecasting performances in terms of relative absolute error (RAE) and root relative squared error (RRSE) measurements shows that the SVR model, with RAE $=$ 12.34% and RRSE $=$ 14.02%, gives a better performance. The results obtained in this study provide information for researchers interested in applying data mining techniques to tourism demand forecasting and help policy makers, government bodies, investors, and managers for their regularization, planning, and investments by way of accurate tourism demand forecasting.

Comparative study of data mining techniques for the authentication of organic grape juice based on ICP-MS analysis

Authenticity is a substantial matter and a current concern of the organic food industry. Organic foods are appreciated by customers because of their benefits to health and friendliness to the environment. However, currently, the most common way for customers to confirm that the organic food they are buying are organic is by certificates and label information, which can be fraudulent. Furthermore, it is interesting to gain insight into organic food composition and visualize which mineral components are fundamental in the differentiation of organic from conventional food. This work addresses these problems using data mining concepts and techniques in a comparative study of organic and conventional food focusing on grape juice, but the proposed methodology can be adapted and employed for analysis of other types of organic food. This article presents a data mining analysis of the elemental composition of 37 grape juice samples collected from different locations in Brazil. The elemental composition of grape juice samples was determined by inductively-coupled plasma-mass spectrometry (ICP-MS). Forty-four elements were determined in the two types of samples, namely organic and conventional grape juice. Special effort was devoted to selecting the variables (elements) that best described each type of grape juice. Predictive models based on support vector machines, neural networks and decision trees were developed to successfully differentiate organic from conventional grape juice samples. We found that, according to the F-score, Chi-square and Random Forest Importance variable selection measures, the components Na, Sn, P, K, Sm and Nd are among the most important variables in the differentiation of organic and conventional grape juice samples. Particularly, the components Na, Sn and K received first, second or third position according to at least two methods. On the other hand, all variable selection methods considered indicated that Ag, Zn, Cr, Be and Pd were among the least important variables for the differentiation of organic and conventional grape juices. SVM yielded an accuracy of 89.18%, both CART and MLP achieved an accuracy of 86.48%.

Improving Effectiveness Of Web Sites UsingIncremental Data Mining Over Clickstreams

The increasing necessity of organizational data exploration and analysis, seeking new knowledge that may be implicit in operational systems, has made the study of data mining techniques gain a huge impulse. This impulse can be clearly noticed in the e-commerce domain, where the analysis of a client’s past behaviours is extremely valuable and may, eventually, bring up important working instruments for determining their future behaviour. Typical databases are continuously changing, which can invalidate some patterns or introduce new ones. Thus, conventional data mining techniques were proved to be inefficient, as they needed to re-execute to update the mining results with the ones derived from the database changes. To solve this problem and ensure a better performance in data mining processes, incremental mining techniques have emerged. In this paper, we analyze some existing incremental mining strategies and models, giving particular emphasis to their application on Web sites, in order to develop models to discover Web users behaviour patterns and automatically generate some recommendations to restructure sites in useful time.