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The growth of academic data size in higher education institutions increases rapidly. This huge volume of data collection from many years contains hidden knowledge, which can assist the improvement of education quality and students performance. Students' performance is affected by many factors. In this study, the data used for data mining were students' personal data, education data, admission data,...
Indonesia have a massive number of SMEs, but with a very low revenue. An alternative to increase revenue is by using internet. Some SMEs already develop their website, but they don't have same navigation. The websites confuse the potential buyers. So, a website's aggregator is essential. This aggregator is made without the owner of the SMEs to register their website, which means it can automatically...
Recent survey shows that heart disease is a leading cause of death in India and in world wide. Significant life savings can be achieved, if a timely and cost effective clinical decision system is developed. Adverse reactions occur if a disease is not diagnosed properly. A clinical decision support system can assist health care professionals for early diagnosis of heart disease from patient's medical...
This paper explores the effectiveness of Particle Swarm Classification technique for tackling a classification problem in an emergent data mining field, called Educational Data Mining. More specifically, it applies Particle Swarm Classification to classify a data set of teachers' classroom questions into the cognitive levels of Bloom's taxonomy. Furthermore, the high dimensionality of questions data...
The widespread availability of multi-core processors and specialized co-processors has generally not been matched by the actual use of parallel software by users. In this work we experimentally verify the simplicity of code parallelization by implementing two kinds of data mining algorithms on two parallel platforms with a view to building upon them in future projects. We use CUDA on a graphics card...
The aim of this paper is to propose a method namely CLUSS — CLUstering and SMOTE Sampling that can improve the prediction performance on multiclass imbalanced problem with students' performance data. Firstly, the clustering approach is used to create a new subset from all majority classes. The new subsets consists of the groups of majority classes instances which have different characteristics. Secondly,...
Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically and scientifically a challenging task for scientists and researchers around the globe. Rainfall is a liquid form of precipitation that depends primarily on humidity, temperature, pressure, wind speed, dew point, and so on. Because rainfall depends on several parameters, its prediction becomes very complex...
Innovations are essential to ride the inevitable tide of revolutions. Most of enterprises are striving to reduce their computing cost through the means of virtualization. This demand of reducing the computing cost has led to the innovation of cloud computing. With the increasing number of companies resorting to employ resources in the cloud, the protection of the users' data is becoming a significant...
In the field of software engineering, which is emerging as the undisputed man of the match in the ever-changing sports of sophistications, the incessant changes effected in software every day have assumed such an alarming proportion causing untold and unimagined paradoxes that it is highly essential to initiate instant and immediate steps to balance this blitz. It does not mean that no endeavor has...
With the rapid development of distance education institution, there exist lots of educational data to be analyzed for improving online teaching. Decision tree is an effective analysis algorithm in data mining. In this paper, decision tree algorithm is used to analyze students' achievement data provided by the Open University of China. The experiment result shows that students have different achievement...
The development of data mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. Cancer classification has improved over the past 20 years; there has been no general approach for identifying new cancer classes or for assigning tumors to known classes (class prediction). Most proposed cancer classification methods...
In ubiquitous environment, too much information exist, and it is not easy to obtain the well classified data from the information. Therefore an algorithm which should be fast and deduce good result is needed. About it, a decision tree algorithm is much useful in the field of data mining or machine learning system for the problem of classification. However sometimes according to several reasons, a...
Although object-based image analysis (OBIA) has been used for detailed classification of urban areas, its attribute selection and knowledge discovery have been time consuming and subjective to analysts' performance. In this study, Data Mining was performed using C4.5 algorithm to select the appropriate attributes for object-based classification. This algorithm provides a decision tree output to represent...
Data streams are continuous, unbounded, usually come with high speed and have a data distribution that often changes with time. It has different issues such as memory, time, Data Processing Model. There is need of handling data streams because of its changing nature, and the data stream may be labeled or it may be unlabelled. Classification is supervised it can only handle labeled data Thus, In this...
In order to extract fresh knowledge out of the data present in a data warehouse, a wide range of knowledge discovery techniques have been provided that process the data in multiple passes. But nowadays, we are facing a challenge of handling massive data in a proper and timely manner so as to extract useful information (knowledge) from streaming data. Such massive streaming data cannot be stored in...
Intrusion detection is the act of detecting unwanted traffic on a network or a device. An IDS can be a piece of installed software or a physical appliance that monitors network traffic in order to detect unwanted activity and events such as illegal and malicious traffic, traffic that violates security policy, and traffic that violates acceptable use policies. However, Intrusion detection systems face...
Cardiovascular diseases related ● Coronary heart disease, Angina pectoris, congestive heart failure, Cardiomyopathy, congenital heart disease are the first cause of death in the Asian world. The health care industry collects a huge amount of data which is not properly mined and put into optimum use resulting in these hidden patterns and relationships often going unexploited. Advanced...
In spite of growing information system widely, security has remained one hard-hitting area for computers as well as networks. In information protection, Intrusion Detection System (IDS) is used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining is an efficient artifice applied to intrusion detection to ascertain a new outline from the...
Patients with liver disease continue to increase and the symptoms of the disease is difficult to detect. Therefore many people suffer from liver damage but they feel healthy, it causes many medical practitioners to often fail to detect the disease. Failure to detect can mislead to improper medication and medical treatment. Therefore accurate detection is necessary to help the medical practitioner...
Hoeffding's bound (HB) has been widely used for node splitting in incremental decision tree algorithms. Many decision-tree algorithms adopt a sliding-window technique to detect concept drift when mining changing data streams. This paper presents a novel node-splitting approach that replaces the traditional HB with a new measure. The new measure is derived from a loss function applied in a cache-based...
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