There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

비만아 부모의 돌봄 경험: q 방법론

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

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Educational Data Mining: A Review of the State of the Art

Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. Statistical inference is the tool that most physicians use for diagnosis, though in many cases it does not appear powerful enough. Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. In this paper, the methodology that consists of combining unsupervised feature selection and grouping to improve the performance of statistical analysis is considered. We assume that the set of attributes used in clustering and statistical analysis phases should be different and not correlated. Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. The proposed methodology has been verified by experiments done on three real datasets of cardiovascular cases. The obtained effects have been evaluated regarding the number of detected dependencies between parameters. Experiment results showed the advantage of the presented approach compared to other feature selection methods and without using clustering to support statistical inference.

/pdf/integrating-correlation-based-feature-selection-and-52797eknhz.pdf

Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis

Market segmentation is one of the most important area of knowledge-based marketing. In banks, it is really a challenging task as data bases are large and multidimensional. In the paper we consider cluster analysis, which is the methodology, the most often applied in this area. We compare clustering algorithms in cases of high dimensionality with noise. We discuss using three algorithms: density based DBSCAN, k-means and based on it two-phase clustering process. We compare algorithms concerning their effectiveness and scalability. Some experiments with exemplary bank data sets are presented.

Clustering algorithms for bank customer segmentation

Personalized e-learning system should be tailored into student needs, which usually differ even among learners, who attend the same course and have similar technical skills. In the chapter, it is proposed the system architecture, in which teaching paths as well as proper layouts are adjusted to groups of students with similar preferences, created by application of clustering techniques. Learner models are based on dominant learning style dimensions, according to which students focus on different types of information and show different performances in educational process. Extension of the model by including usability preferences is investigated. There are examined different clustering techniques to obtain groups of the best quality. It is presented the algorithm that will fulfill tutor requirements especially concerning the choice of parameters. Some experimental results for real groups of students and different algorithms are described and discussed.

Cluster Analysis in Personalized E-Learning Systems

In the paper, the data driven approach for users' modeling in intelligent e-learning system is considered Individual models are based on preferred learning styles dimensions, according to which students focus on different types of information and show different performances in educational process Building individual models of learners allows for adjusting teaching paths and materials into their needs In the presented approach, students are divided into groups by unsupervised classification Application of two-phase hierarchical clustering algorithm which enables tutors to determine such parameters as maximal number of groups, clustering threshold and weights for different learning style dimensions is described Experimental results connected with modeling real groups of students are discussed

Cluster Analysis for Users' Modeling in Intelligent E-Learning Systems

Credits granting are very important parts of banks' activities, as they may give big profits, but there is a big risk connected with making decisions in this area and mistakes may be very costly for financial institutions. The main idea in credit risk evaluation investigations consists of building classification rules that assign properly bank customers as good or bad payers. In the paper, the system based on combination of unsupervised and supervised clas- sification is proposed. In the first step, by using clustering algorithm, clients are segmented into groups with similar features. In the second step, decision trees are built and classification rules, for each group of clients, are defined. To avoid redundancy, different attributes are taken into account during each kind of classification. The proposed approach allows for using different rules within the same data set, and for defining more accurately clients with high risk. The system was tested on the real credit-risk data sets. Some exemplary results concerning different groups of clients are presented.

/pdf/on-integrating-unsupervised-and-supervised-classification-1qrysc9vd2.pdf

Danuta Zakrzewska

Papers

Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis

Clustering algorithms for bank customer segmentation

Cluster Analysis in Personalized E-Learning Systems

Cluster Analysis for Users' Modeling in Intelligent E-Learning Systems

On integrating unsupervised and supervised classification for credit risk evaluation