Visual analytics of heterogeneous data for criminal event analysis VAST challenge 2015: Grand challenge

Chae, Junghoon; Wang, Guizhen; Ahlbrand, Benjamin; Gorantla, Mahesh Babu; Zhang, Jiawei; Chen, Siqaio; Xu, Haidi; Zhao, J. L.; Hatton, William; Malik, Abish; Ko, Sungahn; Ebert, David S.

doi:10.1109/vast.2015.7347654

Cited by 4 publications

(7 citation statements)

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“…Aghabozorgi et al [12] state that density-based clustering has not been used broadly for time series data in the data mining community as it has some complexity. However, we found that many of our surveyed visual analytics papers have adopted density-based methods [34], [42], [48], [57], [64], [66], [67], [69], [71], [73], [74], [77], [78], [89]. Looking at combinations of visualization with clustering algorithms, the surveyed papers indicate that the trend is dominated by trajectory data that often adopts density-based techniques for clustering compared to other clustering algorithms.…”

Section: ) Density-based Methodsmentioning

confidence: 99%

“…DBSCAN has good efficiency on large datasets and aims to discover clusters of arbitrary shapes. For example, Chae et al [74] and Zhao et al [77], in both visual analytics systems, use DBSCAN to group visitors into corresponding clusters. Zhao et al [77] utilize the longest common subsequence (LCS) to measure the similarity of two visitors' sequences before applying DBSCAN.…”

Section: ) Density-based Methodsmentioning

confidence: 99%

“…k-Means has been used to cluster time series data, achieving efficient clustering results due to its speed, simplicity, ease of implementation, and the possibility to assign the desired amount of clusters [43], [136]. Most of the surveyed papers use commonly applied partitioning methods of clustering, especially the k-Means algorithm [34], [36], [38], [43], [52], [58], [74], [75], [77], [78], [84], [86], [87], [89], [90], [95]. k-Means clustering can be performed on multivariate time series, where each time point is considered as a vector and the cluster labels are used as symbols to encode the time series [43].…”

Section: K-meansmentioning

confidence: 99%

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Clustering and Classification for Time Series Data in Visual Analytics: A Survey

et al. 2019

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Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics.

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Section: ) Density-based Methodsmentioning

confidence: 99%

Section: ) Density-based Methodsmentioning

confidence: 99%

Section: K-meansmentioning

confidence: 99%

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Clustering and Classification for Time Series Data in Visual Analytics: A Survey

et al. 2019

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“…This identification of regular configurations and distributions over time is represented by a total number of events and behaviors extracted from a chosen spatial scale. Personal mobility behaviors and movement patterns [324][325][326][327][328][329][330][331][332], behaviors of animals [333,334], pattern changes in climate (weather) and the ozone layer [332,[335][336][337][338][339][340][341], and behavior capture data made through time at often uniform time intervals [135,[342][343][344][345][346] can be regarded as instances of this type of data that take place in specific spatial identification.…”

Section: Time-series Of Spatial Configurations and Distributionsmentioning

confidence: 99%

“…Density-based clustering for time-series data has some advantages; it is a fast algorithm which does not require pre-setting the number of clusters, is able to detect arbitrarily shaped clusters as well as outliers, and uses easily comprehensible parameters such as spatial closeness[329]. Although density-based clustering entails some complexity, many time-series clustering algorithms have adopted this method[288,295,300,308,315,317,318,320,322,324,325,328,329,340].7.4 DeepCluster Method Applied to Biological Time-series Data:A Case StudyThe process of time-series clustering is accompanied by several difficulties and challenges, such as feature representations at different time scales, and distortion by high-frequency perturbations and random noise in time-series data[411]. Time-series data has also shown considerable diversity in relevant features and properties, dimensionality, and temporal scales[412].To overcome these challenges, a deep learning method can be designed to disentangle the data manifolds and allow a clustering method to deal with learned features instead of rawdata.…”

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confidence: 99%

Deep Clustering and Deep Network Compression

Alqahtani¹

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The use of deep learning has grown increasingly in recent years, thereby becoming a much-discussed topic across a diverse range of fields, especially in computer vision, text mining, and speech recognition. Deep learning methods have proven to be robust in representation learning and attained extraordinary achievement. Their success is primarily due to the ability of deep learning to discover and automatically learn feature representations by mapping input data into abstract and composite representations in a latent space. Deep learning’s ability to deal with high-level representations from data has inspired us to make use of learned representations, aiming to enhance unsupervised clustering and evaluate the characteristic strength of internal representations to compress and accelerate deep neural networks.Traditional clustering algorithms attain a limited performance as the dimensionality in-creases. Therefore, the ability to extract high-level representations provides beneficial components that can support such clustering algorithms. In this work, we first present DeepCluster, a clustering approach embedded in a deep convolutional auto-encoder. We introduce two clustering methods, namely DCAE-Kmeans and DCAE-GMM. The DeepCluster allows for data points to be grouped into their identical cluster, in the latent space, in a joint-cost function by simultaneously optimizing the clustering objective and the DCAE objective, producing stable representations, which is appropriate for the clustering process. Both qualitative and quantitative evaluations of proposed methods are reported, showing the efficiency of deep clustering on several public datasets in comparison to the previous state-of-the-art methods.Following this, we propose a new version of the DeepCluster model to include varying degrees of discriminative power. This introduces a mechanism which enables the imposition of regularization techniques and the involvement of a supervision component. The key idea of our approach is to distinguish the discriminatory power of numerous structures when searching for a compact structure to form robust clusters. The effectiveness of injecting various levels of discriminatory powers into the learning process is investigated alongside the exploration and analytical study of the discriminatory power obtained through the use of two discriminative attributes: data-driven discriminative attributes with the support of regularization techniques, and supervision discriminative attributes with the support of the supervision component. An evaluation is provided on four different datasets.The use of neural networks in various applications is accompanied by a dramatic increase in computational costs and memory requirements. Making use of the characteristic strength of learned representations, we propose an iterative pruning method that simultaneously identifies the critical neurons and prunes the model during training without involving any pre-training or fine-tuning procedures. We introduce a majority voting technique to compare the activation values among neurons and assign a voting score to evaluate their importance quantitatively. This mechanism effectively reduces model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Empirically, we demonstrate that our pruning method is robust across various scenarios, including fully-connected networks (FCNs), sparsely-connected networks (SCNs), and Convolutional neural networks (CNNs), using two public datasets.Moreover, we also propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts, with the aim of evaluating the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a se-mantic concept and individual hidden unit representations is utilized to evaluate feature maps’ importance quantitatively. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines.To conclude, we present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a founding contribution to the area of applying deep clustering to time-series data by presenting the first case study in the context of movement behavior clustering utilizing the DeepCluster method. The results are promising, showing that the latent space encodes sufficient patterns to facilitate accurate clustering of movement behaviors. Finally, we identify state-of-the-art and present an outlook on this important field of DTSC from five important perspectives.

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Visual Analytics: A Comprehensive Overview

Cui

2019

IEEE Access

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With the ever-increasing amount of data, the world has stepped into the era of ''Big Data''. Presently, the analysis of massive and complex data and the extraction of relevant information, have been become essential tasks in many fields of studies, such as health, biology, chemistry, social science, astronomy, and physics. However, compared with the development of data storage and management technologies, our ability to gain useful information from the collected data does not match our ability to collect the data. This gap has led to a surge of research activity in the field of visual analytics. Visual analytics employs interactive visualization to integrate human judgment into algorithmic data-analysis processes. In this paper, the aim is to draw a complete picture of visual analytics to direct future research by examining the related research in various application domains. As such, a novel categorization of visual-analytics applications from a technical perspective is proposed, which is based on the dimensionality of visualization and the type of interaction. Based on this categorization, a comprehensive survey of visual analytics is performed, which examines its evolution from visualization and algorithmic data analysis, and investigates how it is applied in various application domains. In addition, based on the observations and findings gained in this survey, the trends, major challenges, and future directions of visual analytics are discussed.

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Visual analytics of heterogeneous data for criminal event analysis VAST challenge 2015: Grand challenge

Cited by 4 publications

References 3 publications

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Deep Clustering and Deep Network Compression

Visual Analytics: A Comprehensive Overview

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