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- Corpus ID: 15013571
Theses of the doctoral ( PhD ) dissertation Data mining and soft computing algorithms for decision support systems
- Published 2013
- Computer Science
20 References
Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using google maps api, improvement of pso algorithm by memory based gradient search - application in inventory management, novel techniques and an efficient algorithm for closed pattern mining, bit-table based biclustering and frequent closed itemset mining in high-dimensional binary data, biclustering of high-throughput gene expression data with bicluster miner, genefuncster: a web tool for gene functional enrichment analysis and visualisation, constrained particle swarm optimization of supply chains, monte carlo simulation based performance analysis of supply chains, impact of molecular mimicry on the clinical course and outcome of sepsis syndrome., mucosal immunity and the intestinal microbiome in the development of critical illness, related papers.
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Educational data mining: a sample of review and study case
Researchers of WOLNM a , ESIME-Z b & CIC c – b, Abstract The aim of this work is to encourage the research in a novel merged field: Educational data mining (EDM). Thereby, two subjects are outlined: The first one corresponds to a review of data mining (DM) methods and EDM applications. The second topic represents an EDM study case. As a result of the application of DM in Web-based Education Systems (WBES), stratified groups of students were found during a trial. Such groups reveal key attributes of volunteers that deserted or remained during a WBES experiment. This kind of discovered knowledge inspires the statement of correlational hypothesis to set relations between attributes and behavioral patterns of WBES users. We concluded that: When EDM findings are taken into account for designing and managing WBES, the learning objectives are improved.
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IOSR Journals
Educational Data Mining is an emerging discipline that focuses on applying Data Mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and institutional effectiveness. A literature review on educational data mining follows, which covers topics such as student retention and attrition, personal recommender systems with in education and how data mining can be used to analyze course management system data. Gaps in the current literature and opportunities for further research are presented.
Computers & Education
Atufenua Maui
Hania Malik
Lakshmiprabha Murali
An emerging interdisciplinary research field known as educational data mining (EDM) is concerned with developing methods and applying data mining techniques for exploring the unique types of data that come from educational systems. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes. This paper explains how the data mining tasks classification, prediction, and clustering can be applied to data taken from an e-learning system. The performance of sixth grade school students are taken for the analysis and present the results achieved with WEKA tool.
Expert Systems with Applications
Alejandro Peña
Sebastián Ventura
Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering.
Proceeding of the 2005 conference on …
A. Merceron
Dave Donnellan
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Sonali Agarwal
International journal of applied research
Mohamed Khaldi
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This thesis first introduces the basic concepts of data mining, such as the definition of data mining, its basic function, common methods and basic process, and two common data mining methods, classification and clustering. Then a data mining application in network is discussed in detail, followed by a brief introduction on data mining ...
Part-II of the thesis is about Implementing Data Mining Techniques in finding the trends of celebrities death causes over the past decade. The database for training is created from the public and ...
PhD Thesis (TeX) on Data Mining Temporal and Indefinite Relations using Numerical Dependencies. University College London - PhdThesis-DataMining/thesis.pdf at master ...
objective in this thesis is to get a deeper knowledge of the data mining area and evalu-ate methods to automatically classify the data in structured personal registries. In this report I'll describe di erent methods and algorithms used in data mining, apply a few selected popular algorithms on two data sets and then discuss the result. i
and librarians. The Theseus data is also continuously updated with new theses so that information regarding degree topics stays up to date. 2.2 Data Mining Overview Data mining is the process of discovering patterns and relationships in large volumes of data by using methods from the areas of computer science, statistics and artificial ...
This is probably the most widely discussed case of data mining in the field of economics. One of the reasons it is widely discussed is not only that data mining was conducted on a large scale, but also that it was done so openly, almost proudly. The concept of data mining had always been a term for bad practice.
ery (data mining) from this data has b ecome v ery imp ortan t for the business and scien ti c-researc h comm unities alik e. This do ctoral thesis in tro duces Query Flo c ks, a general framew ork o v er relational data that enables the declarativ e form ulation, systematic optimization, and e cien t pro cessing of a large class of mining ...
Data mining is concerned with knowledge discovery and finding patterns in. datasets through a process of applying the model to the data [13]. The model, the heart of. the data mining proce ss, is ...
thesis title: developing knowledge based system using data mining techniques for diagnosis and treatment of diabetes by: kedir eyasu advisors: 1. worku jimma (phd candidate) 2. takele tadese (msc) submitted to: information science department jimma, ethiopia november, 2018
In automatic text data mining,current challenge was to have a rich text type classification. By "rich" classification means to go beyond basic categories like "string", "number", "date", etc that are common in programming languages and databases. A "rich" classification should be able to detect categories of
The first and one of the more important aspects to data mining and EDM is the method data is collected, and as well what data is collected [4, 12, 16]. With the growth of technology used inside of a classroom, data collection has for the most part become automated receiving student attempts, grades, and course data.
bioinformatics, data mining techiniques were not as widely used in the social and health-care sciences until recently. Indeed, there is a growing interest to develop data mining techniques specifically tailored for the unique discovery problems arising in many fields such as the social sciences (Attewell et al., 2015).
A thesis submitted to the University of Manchester Institute of Science and Technology for the degree of Doctor of Philosophy 2000 ... Data Mining is a broad term for a variety of data analysis techniques applied to the problem of extracting meaningful knowledge from large, noisy databases. An important
This conference1 presents full papers (7-8 pages), short papers (4-6 pages) posters (2 pages) and demos related to the use of educational data mining (2 pages). In the figure below the increase in the number of jobs is evident: Table 1. Accepted works in the International Conference on Educational Data Mining. Papers.
Theses of the doctoral ( PhD ) dissertation Data mining and soft computing algorithms for decision support systems @inproceedings{Kirly2013ThesesOT, title={Theses of the doctoral ( PhD ) dissertation Data mining and soft computing algorithms for decision support systems}, author={Andr{\'a}s Kir{\'a}ly}, year={2013}, url={https://api ...
Consult the top 50 dissertations / theses for your research on the topic 'Data mining.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
Address: 20, Myasnitskaya Street, Moscow 101000, Russia. Abstract. This work analyzes the intellectual structure of data mining as a scientific discipline. T o do this, we use. topic analysis ...
This review gives an overview of the challenges of large time series and the proposed problem solving approaches from time series data mining community. We illustrate the most impor-tant techniques with Google trends data. Moreover, we review current research directions and point out open research questions.
Researchers of WOLNM a , ESIME-Z b & CIC c - b, Abstract The aim of this work is to encourage the research in a novel merged field: Educational data mining (EDM). Thereby, two subjects are outlined: The first one corresponds to a review of data mining (DM) methods and EDM applications. The second topic represents an EDM study case.
The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm. Download Full-text.
Regression prediction modeling is the task of approximating a mapping function (f) from one or. more input variables (x) to a continuous output variable (or variables) (y) which is a real value and. often comes in the form of a quantity, such as amounts and sizes. The regression model is evaluated.
PDF | Data mining is a process which finds useful patterns from large amount of data. ... Thesis. Full-text available ... Data mining is proved to be one of the important tools for identifying ...
1.2. Data mining techniques in decision support systems. In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques.