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Researchers in higher education are beginning to explore the potential of data mining in analyzing data for the purpose of giving quality service and needs of their graduates. Thus, educational data mining emerges as one tools to study academic data to identify patterns and help for decision making affecting the education. This paper predicts the employability of IT graduates using nine variables...
We present in this paper the analysis results of prominent educational characteristics differentiating people from the two regions in the world: advanced economies versus east Asia and the pacific countries. The automatic multivariate analysis of classification trends has been demonstrated through the visual data mining tool called KNIME. We found from the empirical studies that from the years 1950...
As the number of cyber attacks have increased, detecting the intrusion in networks become a very tough job. For network intrusion detection system (NIDS), many data mining and machine learning techniques are used. However, for evaluation, most of the researchers used KDD Cup 99 data set, which has widely criticized for not showing current network situation. In this paper we used a new labelled network...
Air pollutants are really a hazardous problem in Bangladesh. This paper works on the relationship between the pollutants and the admittance of patients in the medical facilities and analyzes the reason behind the increase of the disease rate in the hospitals. The research collected medical data from the medical center named National Institute of Disease of the Chest and Hospital (NIDCH) that is located...
Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefined classes. Various techniques for classification exists which classifies the data using different algorithms. Each algorithm has its own area of best and worst performance. This paper...
Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction...
From a large amount of data, significant knowledge is discovered by means of applying techniques in the knowledge management process and those techniques is known as Data mining techniques. For a specific domain, a form of knowledge discovery called data mining is necessary for solving the problems. The classes of unknown data are detected by the technique called classification. Neural networks, rule...
The aim of this study is to compares some classification techniques used to predict the performance of student. It is helps to analyse the slow leaner in the semester exams that are likely study in poor which are used to improve their skill as early to achieve the goal in end semester. The task can be processed based on the several attributes to predict the performance of the student activity respectively...
Data mining approaches have been used in business purposes since its inception; however, at present it is used successfully in new and emerging areas like education systems. Government of Bangladesh emphasizes the need to improve the education system. In this research, we use data mining approaches to predict students' final outcome, i.e., final grade in a particular course by overcoming the problem...
Large amount of medical data leads to the need of intelligent data mining tools in order to extract useful knowledge. Researchers have been using several statistical analysis and data mining techniques to improve the disease diagnosis accuracy in medical healthcare. Heart disease is considered as the leading cause of deaths worldwide over the past 10 years. Several researchers have introduced different...
Today, big data is not only the data scenario with large volume, but also high-speed and changing all the time. Such data streams commonly exist in Smart Grid facilities. Decision tree as one of the most widely-used analysis methods, has been applied in the decision support system for smart grid. This paper proposes a two-level classifier combining cache-based classifier and incremental decision tree...
Real-time data streams classification is a challenging data mining task. In real-time streaming environments concepts of instances might change at any time such as weather predictions, astronomical and intrusion detection etc. To address this issue, we present an adaptive ensemble classifier for data streams classification, which uses a set of decision trees for mining complex noisy instances in data...
Decision tree (DT) and naïve Bayes (NB) classifiers are useful, efficient and commonly used for solving multi-class classification tasks in machine learning and data mining. In this paper, we introduce an adaptive naïve Bayes tree (NBTree) algorithm for scaling up the classification accuracy of multi-class classification problems, which considers the attributes that are used in the decision tree for...
In this paper we present a novel technique called iDMI that imputes missing values of a data set by combining a decision tree algorithm (DT) and an expectation-maximization (EMI) algorithm. We first divide a data set into horizontal segments through applying a DT algorithm such as C4.5, and then apply an EMI algorithm on each segment in order to impute the missing values belong to the segment. If...
The paper presents a generic probabilistic framework for assessing the accuracy of online prediction of power system transient stability based on phasor measurement unit (PMU) measurements and data mining techniques. It allows fair comparison of different data mining models in terms of the accuracy of the prediction. To illustrate the concept, a decision tree (DT) method is used as an example of a...
In the last decade symbolic representations approaches have been proposed for knowledge discovery in time series. However, the conventional symbolic methods ignore the temporal order of symbols, so this core feature of time series is lost. In this paper, to treat this problem we present a symbolic representation method to incorporate the temporal information in the symbols. The proposed method was...
One of the key success factors of lending organizations in general and banks in particular is the assessment of borrower credit worthiness in advance during the credit evaluation process. Credit scoring models have been applied by many researchers to improve the process of assessing credit worthiness by differentiating between prospective loans on the basis of the likelihood of repayment. Thus, credit...
StarCraft is a real-time strategy (RTS) game and the choice of strategy has big impact on the final results of the game. For human players, the most important thing in the game is to select the strategy in the early stage of the game. Also, it is important to recognize the opponent's strategy as quickly as possible. Because of the “fog-of-war” in the game, the player should send a scouting unit to...
Violations of listed companies to disclose accounting information will mislead the ordinary investors seriously and bring huge losses to investors. Therefore, it is particularly necessary to analyze and identify the violations of listed companies based on scientific and effective methods to avoid investment risks in advance. In this paper, we firstly use t-statistic to select eight useful and characteristic...
Identifying review manipulation has become one of hot research issues in e-commerce because more and more customers make their purchase decisions based on some personal comments from virtual communities and e-business websites. Customers consider these personal reviews are more reliable than the existing internet advertisements. Consequently, some enterprises attempt to create fake personal comments...
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