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Data mining can be used in various fields' i.e. mobile computing, web mining, expert predictions, crime analysis, engineering, management and medicine. In medical field, data mining techniques can be used by the researchers for the diagnosis and prediction of various diseases. A framework is proposed to predict Syncope Disease using Ensemble technique that contains Naïve Bayes, Gini Index and Support...
Cardiovascular disease is a worldwide health problem and according to American Heart Association (AHA), it also causes an approximate death of 17.3 million each year. Therefore early detection and treatment of asymptomatic cardiovascular disease which can significantly reduce the chances of death. An important fact regarding such life-threatening disease prognosis is to identify the patient's physical...
In major colleges and universities, in order to mobilize students enthusiasm for studying and participating in extracurricular activities, all colleges make an evaluation on students comprehensive quality and set different rewards regulations for the various level. The main way is to provide financial incentives, they distribute scholarship for students of meeting requirements. The Decision Tree algorithm...
Data mining is commonly used in the healthcare industry and managing Intensive Care Unit (ICU) is no exception. This study aims to examine how data mining techniques can be employed to predict mortality and length of stay in an ICU and to evaluate various classification techniques. Real-life healthcare datasets, like MIMIC 2, incorporate an unbalanced distribution of sample sizes, which means that...
As blogs widely spread, the need to extract information is necessary in order to deal with different issues such as social, political, criminal and others. This research takes off from Gharehchopogh et al. [2], [3] who used the C4.5 and K-Nearest Neighbor (K-NN) algorithms to classify bloggers whether they are professional or otherwise from the Kohkilooyeh and Boyer Ahmad province in Iran. As a comparative...
Customer churn is one of the main problems in the telecommunications industry. Several studies have shown that attracting new customers is much more expensive than retaining existing ones. Therefore, companies are focusing on developing accurate and reliable predictive models to identify potential customers that will churn in the near future. The aim of this paper is investigating the main reasons...
This paper based on the analysis of the basic meaning in data mining and the structure of decision tree uses the decision tree algorithm — C4.5 to establish a soil quality grade prediction model and combines the soil composition in Lishu to be a training sample. C4.5 algorithm also expresses the acquired knowledge by means of quantitative rules. The experiment results manifest that the expression...
Security risks in the network grow with increase in size. The attacks on networks have increased in recent past tremendously and require efficient intrusion detection systems. Data mining have been used for intrusion detection and have gained much popularity. This paper presents a novel approach to classify intrusion attacks. The central idea is to apply alternating decision trees (ADT) to intrusion...
Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims...
This paper presents a data mining technique for qualitative analysis of Hodgkin-Huxley model of cell excitability. Such problem cannot be solved analytically. Therefore we apply Monte-Carlo techniques for the generation of model parameters, and use data mining algorithm for classification of learning tuples obtained. As a result we attain a decision tree capable of classifying the excitability depending...
In order to solve multi-class classification problem in real world, we improved TSVM in this paper. We combined LSTSVM with partial binary tree to improve classification efficiency. Binary tree hierarchy can solved the inseparable regional issues in OVO-SVM and OVA-SVM classification. Experimental results show that it improved the classification accuracy. It also has better speed-up ratio than the...
Data mining is the procedure of breaking down data from unlike perspectives and resuming it into useful information. It is very important in the field of classification of the objects. It has been fruitfully applied in expert systems to get knowledge. We can determine appropriate classification of unknown objects according to decision tree rules by applying inductive methods to the given values of...
An optimization classification algorithm for MRI images of premature brain injury is introduced. Based on the shortcomings of the classical ID3 algorithm in dealing with the continuous attributes of medical image, the new algorithm selects the testing feature by comparing the information gain ratio and adds the handling methods for filling null values. Then it discrete the continuous attributes by...
This paper examined the students' history of accessing the university Learning Management System (LMS) data. Classification techniques are used to build an educational model based on Knowledge Discovery in Databases (KDD) to predict learner's behavior. It identified the most valuable influencer for learning outcomes of the learners; it generated prediction models using the J48 decision tree algorithm...
Extraction of relevant Information from data Is a challenging task. Many times an analyst may end up with an erroneous classifier because of huge, redundant, unreliable and noisy data. It may also be due to misinterpretation of results and usage of inappropriate techniques for a specific situation. In our study, we have investigated the two main approaches in data mining which are Decision Tree (J48...
Data mining is a powerful concept with great potential to predict future trends and behavior. It refers to the extraction of hidden knowledge from large datasets using techniques like statistical analysis, machine learning, clustering, neural networks and genetic algorithms. Hybrid algorithms for data mining are a logical combination of multiple pre-existing techniques to enhance performance and provide...
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
An accurate prediction of crude oil output is crucial to oilfield enterprise in reasonable production arrangement and the production management improvement. This paper proposes a RS-C4.5 data mining method based on the rough set theory and decision tree C4.5 algorithm to predict the crude oil output. Firstly, relevant data of crude oil production is pre-processed by K-Means algorithm to obtain a discrete...
With the rapid development of the society of China, a large number of land problems such as unused land or inefficient used land for construction exist in the process of land usages, which leads to a waste of massive land resources. In the management of land resources, not only solving the existing problems of the land, but also prediction of the problems of the land and prevention on land abuse are...
MS Visual Studio 2010 is used as a development tool and MS SQL Server 2008 database is also selected to design a universal set of Mongolian medicine prescription data-mining system which provides two language versions of Mongolian and Chinese. System can apply code query, the DataGridView controls, DataViews data objects, partially redundant data objects and many other technologies to fast queries...
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