Usage Of Data Mining Techniques Research Articles

Application and performance of data mining techniques in stock market: A review

SummaryPrediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

Data Mining Techniques with Electronic Customer Relationship Management for Telecommunication Company

Organizations must improve decisional quality, and the continuous usage of data mining techniques is a crucial issue for management. This issue mostly involves an individual's motivation to engage in the behavior. This could perhaps be characterized in terms of the working regimen. technology utilization and employee activity are the two main difficulties that this dilemma revolves around. This study aims to address the aspect associated with data mining and E-CRM in the telecom industry. The methods that are used in the current study, analysis studies of the data mining techniques are applied to E-CRM that has been identified. Moreover, PHP with the update of the DeLone and McLean methods has been used in the current study. The results show the significance in affecting the continuance used intention of data mining techniques. User satisfaction, technology, and data mining are critical predictors of employment intentions.

Efficient Data Mining Techniques for Heart Disease Prediction and Comparative Analysis of Classification Algorithms

Data mining techniques are used to extract interesting patterns and discover meaningful knowledge from huge amount of data. There has been increasing in usage of data mining techniques on medical data for determining useful trends and patterns that are used in analysis and decision making. About eighty percent of human deaths occurred in low and middle-income countries due to heart diseases. The healthcare industry generates large amount of heart disease data which are not organized. These data make the prediction process more complicated and voluminous. Data mining provides the techniques for fast and accurate transformation of data into useful information for heart diseases prediction. The main objectives of this research is to predict heart diseases more accurately using Naïve Bayes, J48 Decision Tree, Neural Network, Random Forest classification algorithms and compare the performance of classifiers. The research uses raw dataset for performance analysis and the analysis is based on Weka Tool. This research also shows best technique from them which is Random Forest on the basis of accuracy and execution time.

An e-health decision support framework to predict the heart disorders

The current evolutionary usage of data mining techniques can be imparted for the development of medical applications to analyse the health metric. The web-based decision support framework proposed in this work would provide the pre-guidance report based on the decision generated by Bayesian network analysis over the disease dataset. The report is generated in adherence to the mined disease patterns over the patient's non-medical and medical factors which are obtained from the past medical records and predict the possibility of getting the disease for the given similar health metrics. The Bayesian model builds a decision model by analysing the casual intervention effects of the non-medical and medical factors of each individual. The decision model would generate a pre-guidance health report based on the analysed probabilistic chances of getting the heart disorder. The predicted report is a prognostic analysis of the health metric of the individual and suspects their possibility of getting affected by heart disease.

An e-health decision support framework to predict the heart disorders

The current evolutionary usage of data mining techniques can be imparted for the development of medical applications to analyse the health metric. The web-based decision support framework proposed in this work would provide the pre-guidance report based on the decision generated by Bayesian network analysis over the disease dataset. The report is generated in adherence to the mined disease patterns over the patient's non-medical and medical factors which are obtained from the past medical records and predict the possibility of getting the disease for the given similar health metrics. The Bayesian model builds a decision model by analysing the casual intervention effects of the non-medical and medical factors of each individual. The decision model would generate a pre-guidance health report based on the analysed probabilistic chances of getting the heart disorder. The predicted report is a prognostic analysis of the health metric of the individual and suspects their possibility of getting affected by heart disease.

Mining Severe Priority Bugs in Software Maintenance

Maintenance of open source software is a hectic task as the number of bugs reported is huge. The number of projects, components and versions in an open source project also contribute to the number of bugs that are being reported. Classification of bugs based on priority and identification of the suitable engineers for assignment of bugs for such huge systems still remains a major challenge. Bugs that are misclassified or assigned to engineers who don’t have the component expertise, drastically affect the time taken towards bug resolution. In this paper we have explored the usage of data mining techniques on the classification of bugs and assignment of bugs to engineers.Our focus was on classifying bugs as either severe or non-severe and identification of engineers who have the right expertise to fix the bugs. The prediction of bug severity and identification of engineers were done by mining bug reports from JIRA, an open source software bug tracking tool. The mining process yielded positive results and will be a decision enhancer for severe bugs in the maintenance phase

Predicting hyperlipidemia using enhanced ensemble classifier

Advancement in medical technology has resulted in bulk creation of electronic medical health records. These health records contain valuable data which are not fully utilized. Efficient usage of data mining techniques helps in discovering potentially relevant facts from medical records. Classification plays an important role in disease prediction. In this paper we developed a prediction model for predicting hyperlipidemia based on ensemble classification. Support Vector Machine, Naïve Bayes Classifier, KNN Classifier and Decision Tree method are combined for developing the ensemble classifier. Performance of each classifier is evaluated separately. An overall accuracy of 97.07% has been obtained by using ensemble approach which is better than the performance of each classifier.

Survey of Data Mining Techniques used in Healthcare Domain

Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.

Mining Association Rules from Empirical Data in the Domain of Education

The data mining techniques and their applications are widely recognized as powerful tools in various domains. In the domain of education there is a variety of data of various types that are collected. The important question is: Is it possible to process collected data with the data mining technique and what are main advantages of data mining and e-learning interaction? If an e-learning system accumulates a huge volume of data, then it is possible to deploy techniques and tools from the domain of data mining in order to gain valuable information. The research presented in this paper is conducted on real-life data that originates from the Balkan region. The software system Weka is used to generate association rules. The main result of this research is the assessment of the parameters that are associated with the opinion that computer skills will be helpful in the future, from the students point of view. This result is very important because it gives the exact insight to computer technology usage in the Balkans schools. Furthermore, some advantages of the usage of data mining techniques in the domain of education are determined.