Coupled Itemset Mining with Mixture of Clusters


Coupled Itemset Mining with Mixture of Clusters – This paper proposes a method for generating reusable, scalable, high-quality, distributed multi-domain image datasets. We propose a new approach that consists of two parts. The first part is to partition the domain into clusters to reduce the number of redundant features. The second part is to construct a new object detector, which is able to detect the most common features over a large number of objects. Each cluster is then partitioned into a set of cluster clusters according to the proposed algorithm. The proposed method performs well in many real-world applications, such as image classification, anomaly detection, visual search and retrieval, and semantic segmentation, and can be easily incorporated into the existing approaches for both applications. Experiments on standard datasets demonstrate that the proposed approach is feasible and efficient: it outperforms existing state-of-the-art methods.

Most state-of-the-art methods for segmentation from images are based on convolutional neural networks (CNN) and use hand-crafted features of the object. In this work, we propose a CNN method based on image features. The proposed CNN model extracts the image features as ground truth, which is obtained by a CNN-based CNN model. We evaluate the CNN model using a computer vision program (C2C) and a segmentation tool (DVS). We demonstrate that the proposed CNN model achieves state-of-the-art performance in terms of CNN-based segmentation rate.

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Coupled Itemset Mining with Mixture of Clusters

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