ATMs enable the public to perform €nancial transactions. Banks try to strategically position their ATMs in order to maximize transactions and revenue. In this paper, we introduce a model which provides a score to an ATM location, which serves as an indicator of its relative likelihood of transactions. In order to eciently capture the spatially dynamic features, we utilize two concurrent predic...

Documents in HTML format have many features to analyze, from the terms in special sections to the phrases that appear in the whole document. However, it is important to decide which feature contributes the most to separate documents according to classes. Given this information, it is possible not to include certain feature in the representation for the document, given that it is expensive to co...

1. Texture segmentation can lead to multiscale outputs in which the partitions at successive scales are nested. 2. We can incorporate hierarchical segmentation into a K-Means clustering technique by steadily relaxing inter-cluster distances. 3. Thus, it is possible to hierarchically segment images based solely on texture measurements. 4. This hierarchical, multiscale segmentation is useful in i...

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...