On the Convergence of K-means Clustering

On the Convergence of K-means Clustering – K-means is one of the fastest evolving data mining algorithms. It is an algorithm that is able to perform clustering and other computationally intensive experiments while being relatively efficient. This paper presents an experimental evaluation of K-means using synthetic and real data from KDDU. KDDU used a synthetic and real data set for training the algorithm to produce data samples and a real data set for testing the performance of K-means with real data. The simulated data set was used to generate a K-means dataset with a variety of conditions. The dataset size and accuracy was tested using an automated system designed to detect anomalies and analyze the impact of anomalies. This paper presents the experimental results for KDDU and simulated data to illustrate the utility of K-means and the performance of KDDU on synthetic data sets.

We investigate methods for unsupervised learning of video-based motion segmentation from images. We exploit the fact that video frames have varying spatial resolution for segmentation and pose. Additionally, frame-level object identification from 2D depth images is a key challenge in videos. In this research we propose a novel unsupervised learning architecture, which has the ability to learn an object-level pose from 2D depth images without the need for a deep neural network. Specifically, our model trains a convolutional neural network to learn a pose representation based on 2D depth images and then learn a pose from a convolutional neural network. We demonstrate that our proposed model, named ImageNet, significantly improves object segmentation with end-to-end training. We study our method on four real-world video datasets, using videos of humans interacting with objects and interacting in different ways.

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On the Convergence of K-means Clustering

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  • An Empirical Comparison between the Two Automatic Forests for Time-Frequency Forecasting

    Object Detection and Classification for Real-Time Videos via Multimodal Deep Net PruningWe investigate methods for unsupervised learning of video-based motion segmentation from images. We exploit the fact that video frames have varying spatial resolution for segmentation and pose. Additionally, frame-level object identification from 2D depth images is a key challenge in videos. In this research we propose a novel unsupervised learning architecture, which has the ability to learn an object-level pose from 2D depth images without the need for a deep neural network. Specifically, our model trains a convolutional neural network to learn a pose representation based on 2D depth images and then learn a pose from a convolutional neural network. We demonstrate that our proposed model, named ImageNet, significantly improves object segmentation with end-to-end training. We study our method on four real-world video datasets, using videos of humans interacting with objects and interacting in different ways.


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