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In recent years the social network analysis has been increased, in this paper we focus mining the small network formed by social gathering or business meeting. The main objective is to discover the existence of correlation and influence among the group of people by analysing their social affinity and emotions, currently we are interested only in facial emotions. Whereas the approach for emotion detection...
Massive Open Online Course(MOOC) is undergoing explosive growth recently, both the number of MOOC platforms and courses are increasing dramatically during these years. One of the major concerns in MOOC is high dropout rate, we study dropout prediction in MOOCs, using student's learning activities data in a period of time to measure how likely students would drop out in next couple of days. We collect...
This article deals with the data mining process on a database containing the strategic steps made during complex problem solving in a computer-based assessment environment. The used database was constructed based on the log-files generated by the computer-based assessment software and published on-line by PISA. Preprocessing, attribute extraction and classification of the data by naïve Bayes classifier...
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...
Due to the recent emergence of Big Data, it is essential to develop techniques to reduce the processing time of such Big Data. In this paper, we propose to reduce the dimensionality of objects' feature vectors by discovering their contrast sets. Contrast set mining aims at finding a set of rules that best distinguish the instances of different user-defined groups. Thus, contrast sets are conjunctions...
Aiming at the particularity of MRI images, the ARC algorithm which is suitable for medical image processing is present. The new method introduces the bi-support association rules based on FP-tree. In the process of generating the rule of association class and constructing the FP-tree, the maximum support is introduced on the premise of minimal support. It can make the support of discovered rules be...
The paper presents a new approach for processing of rhinomanometric signals based on F-transform approximation of phase diagrams. Methods of nonlinear dynamics for processing of time series allow us to obtain a significant features of rhinomanometric signals. Research indicated that the results of classification with F-transform approximation is more accurate than results of classification with FFT...
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...
Poverty was a problem that faced by many developing countries, especially Indonesia. One way to resolve the issue of poverty through social assistance provided by the government. Besides that, knowing the factors affecting poverty in the region was also important to determine the strategic plan to reduce poverty in Indonesia. Data mining approach was used to determine the classification model. The...
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...
Research papers are often referred by researchers. But finding the desired or relevant research paper quickly and accurately is very difficult. As there are lot of research papers in given dataset and keeps enormously increasing. There is a need to avail automated processing approach for tackling such a huge volume of dataset to retrieve relevant papers accurately. This paper proposes a framework...
This paper studies the supervised classification of electroencephalogram (EEG) brain signals to identify persons and their activities. The brain signals are obtained from a commercially available and modestly priced wearable headband. Such wearable devices generate a large amount of data and due to their attractive pricing structure are becoming increasingly commonplace. As a result, the data generated...
Analysts of different fields have shown a good interest in data mining. Data mining is the process of inferring useful patterns from the huge amount of data. Regarding data storage and management process, classical statistical models are however protective. Big data is a popular terminology which is intermittently discussed in the present day, used to describe the enormous quantity of data that may...
In recent few years, social networking site and information technology has become ubiquitous. This technology improvement is affecting the social and economic life of human beings more than anything. The social effects of the faster internet are directly linked to the dependence of today's generation on devices such as high speed internet mobile devices and social networking sites. Lately these social...
The students have setup their goals before starting their engineering studies. To achieve their goals they need to succeed their engineering examinations with good marks and sit in the competition to get good job. The knowledge regarding success rate of students and factors affecting their performance is hidden in educational data set. Extraction of knowledge using data mining techniques helps students...
Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques...
Performing data mining tasks on raw time series is inefficient as these data are high-dimensional by nature. Instead, time series are first pre-processed using several techniques before the different data mining tasks can be performed. In general, there are two main approaches to pre-process time series. The first is what we call landmark methods. These methods are based on finding characteristic...
In recent years, with the gradual development of mobile Internet technology, the number of mobile applications increases dramatically. Users facing numerous mobile applications are often caught off guard. It is necessary to automatically classify the applications according to the applications' information, so as to recommend appropriate applications to users. However, the text information directly...
Big data analytics are very fruitful for solving problems in cybersecurity. We have analyzed modern trends in intelligent security systems research and practice and worked out a syllabus for a new university course in the area of data mining and machine learning with applications to cybersecurity. The course is for undergraduate and graduate students studying the cybersecurity. The main objective...
Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition...
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