Efficient Data Stream Clustering With Sliding Windows Based on Locality-Sensitive Hashing

Youn, Jonghem; Shim, Junho; Lee, Sang-goo

doi:10.1109/access.2018.2877138

Cited by 24 publications

(13 citation statements)

References 41 publications

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“…Clustering plays a significant role in the data-stream mining process [42,[45][46][47][48][49][50][51]. In recent years, many researchers have proposed density-based data stream clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

et al. 2020

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In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The gridbased method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality. INDEX TERMS Clustering, data stream, evolving, grid-based method, core-micro-cluster, online.

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Section: Introductionmentioning

confidence: 99%

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

et al. 2020

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“…Generally, according to whether label information is utilized, existing hashing-based image-text methods can be classified as unsupervised methods and supervised methods. Unsupervised methods [13]- [22] utilize only the image-text pair, including co-occurrence information, to explore their semantic correlation in the shared image-text feature representation space. However, these methods cannot take advantage of semantic label information, i.e., cannot exploit class labels to preserve the intermodal and intramodal correlations of image-text data from the original feature space, which deteriorates the image-text search performance.…”

Section: Introductionmentioning

confidence: 99%

Supervised Matrix Factorization Hashing With Quantitative Loss for Image-Text Search

et al. 2020

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Image-text hashing approaches have been widely applied in large-scale similarity search applications due to their efficiency in both search speed and storage efficiency. Most recent supervised hashing approaches learn a hash function by constructing a pairwise similarity matrix or directly learning the hash function and hash code (i.e.,1 or-1) procedure based on class labels. However, the former suffers from high training complexity and storage cost, and the latter ignores the semantic correlation of the original data, both of which prevent discriminative hash codes. To this end, we propose a novel discrete hashing algorithm called supervised matrix factorization hashing with quantitative loss (SMFH-QL). The proposed SMFH-QL first generates hash codes via the class label, avoiding the construction of a pairwise similarity; then, matrix factorization is used to design hash codes from original image-text data, thereby eliminating the impact of class labels and reducing the quantization error. Moreover, we introduce a quantitative loss function term to learn hash codes by incorporating class labels and the original data information, facilitating learning a similarity-preserving hash function in image-text search. Extensive experiments show that SMFH-QL outperforms several existing hashing methods on three representative datasets. INDEX TERMS Image-text search, supervised hashing, hash function, hash codes, quantitative loss function.

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“…Fortunately, Deep Neural Networks (DNNs), especially Convolutional Neural Networks (CNNs) have been widely used in the computer vision field [17] and have shown their powerful feature extraction capabilities. Inspired by this, some learning based hashing methods [18][19][20]22] that adopt convolutional neural networks as the nonlinear hashing functions to enable end-to-end learning of learnable representations and hash codes, have demonstrated satisfactory retrieval performance on many benchmark datasets. Despite recent learning based hashing methods achieving significant progress in image retrieval, there are still some limitations to their usage, e.g., the label information is a simple construction of the similarity matrix, and does not make full use of the multiple label information of the data points [23].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Deep Semantic Quantization for Image Retrieval

et al. 2019

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Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing technique in many applications. However, we argue that the current deep learning based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as Asymmetric Deep Semantic Quantization (ADSQ). ADSQ is implemented using three stream frameworks, which consist of one LabelNet and two ImgNets. The LabelNet leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two ImgNets, they each adopt the same convolutional neural network structure, but with different weights (i.e., asymmetric convolutional neural networks). The two ImgNets are used to generate discriminative compact hash codes. Specifically, the function of the LabelNet is to capture rich semantic information that is used to guide the two ImgNets in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, ADSQ can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. Experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed ADSQ can outperforms current state-of-the-art methods.

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Efficient Data Stream Clustering With Sliding Windows Based on Locality-Sensitive Hashing

Cited by 24 publications

References 41 publications

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

Supervised Matrix Factorization Hashing With Quantitative Loss for Image-Text Search

Asymmetric Deep Semantic Quantization for Image Retrieval

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