Although all university majors are prominent, and the necessity of their presence is of no question, they might not have the same priority basis considering different resources and strategies that could be spotted for a country. Their priorities likely change as the time goes by; that is, different majors are desirable at different time. If the government is informed of which majors could tackl...

A C♯ implementation of a generalized k-means variant called relational k-means is described here. Relational k-means is a generalization of the well-known k-means clustering method which works for non-Euclidean scenarios as well. The input is an arbitrary distance matrix, as opposed to the traditional k-means method, where the clustered objects need to be identified with vectors.

In this paper Knockout Refinement Algorithm (KRA) is proposed to refine original clusters obtained by applying SOM and K-Means clustering algorithms. KRA Algorithm is based on Contingency Table concepts. Metrics are computed for the Original and Refined Clusters. Quality of Original and Refined Clusters are compared in terms of metrics. The proposed algorithm (KRA) is tested in the educational ...

The K-means algorithm is quite sensitive to the cluster centers selected initially and can perform different clusterings depending on these initialization conditions. Within the scope of this study, a new method based on the Fuzzy ART algorithm which is called Improved Fuzzy ART (IFART) is used in the determination of initial cluster centers. By using IFART, better quality clusters are achieved...

We study fast learning rates when the losses are not necessarily bounded and may have a distribution with heavy tails. To enable such analyses, we introduce two new conditions: (i) the envelope function supf∈F |` ◦ f |, where ` is the loss function and F is the hypothesis class, exists and is L-integrable, and (ii) ` satisfies the multi-scale Bernstein’s condition on F . Under these assumptions...

This paper studies the k-means++ algorithm for clustering as well as the class ofD sampling algorithms to which k-means++ belongs. It is shown that for any constant factor β > 1, selecting βk cluster centers by D sampling yields a constant-factor approximation to the optimal clustering with k centers, in expectation and without conditions on the dataset. This result extends the previously known...

The k-means algorithm is a widely used clustering technique. Here we will examine the performance of multiple implementations of the k-means algorithm in different settings. Our discussion will touch on the implementation of the algorithm in both python and C, and will also mention a 3rd party package for the k-means algorithm that is also written in C but provides python bindings. We will then...

The assessment of visible differences in leaf shape between plant species or mutants (phenotyping) plays a significant role in plant research. This paper investigates the application of unsupervised data clustering techniques for phenotype screening to find hidden common shape categories. A set of two wildtypes and seven mutations of Arabidopsis acted as a test case. K-Means, NG, GNG, SOM and A...

In this paper, we define and study a new notion of stability for the k-means clustering scheme building upon the field of quantization of a probability measure. We connect this definition of stability to a geometric feature of the underlying distribution of the data, named absolute margin condition, inspired by recent works on the subject.

This paper presents the integration between the quantitative target approximation (qTA) model and the unsupervised clustering technique to study Thai tones. The qTA model simulates F0 production on the basis of articulation process. Parameters extracted from the F0 of Thai speech by analysisand-synthesis method were further analyzed by K-means clustering. The number and form of pitch target wer...