2017 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第3页

On Utility of Temporal Embeddings in Skill Matching 时间嵌入在技能匹配中的应用

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.37

Manisha Verma, Nathan Francis

引用次数: 2

Predicting Users’ Search Behavior Using Stochastic Multi-mode Network Models 基于随机多模网络模型的用户搜索行为预测

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.25

Shohei Umehara, K. Eguchi

引用次数: 0

Robust Self-Tuning Sparse Subspace Clustering 鲁棒自调优稀疏子空间聚类

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.117

Guangtao Wang, Jiayu Zhou, Jingjie Ni, Tingjin Luo, Wei Long, Hai Zhen, G. Cong, Jieping Ye

{"title":"Robust Self-Tuning Sparse Subspace Clustering","authors":"Guangtao Wang, Jiayu Zhou, Jingjie Ni, Tingjin Luo, Wei Long, Hai Zhen, G. Cong, Jieping Ye","doi":"10.1109/ICDMW.2017.117","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.117","url":null,"abstract":"Sparse subspace clustering (SSC) is an effective approach to cluster high-dimensional data. However, how to adaptively select the number of clusters/eigenvectors for different data sets, especially when the data are corrupted by noise, is a big challenge in SSC and also an open problem in field of data mining. In this paper, considering the fact that the eigenvectors are robust to noise, we develop a self-adaptive search method to select cluster number for SSC by exploiting the cluster-separation information from eigenvectors. Our method solves the problem by identifying the cluster centers over eigenvectors. We first design a new density based metric, called centrality coefficient gap, to measure such separation information, and estimate the cluster centers by maximizing the gap. After getting the cluster centers, it is straightforward to group the remaining points into respective clusters which contain their nearest neighbors with higher density. This leads to a new clustering algorithm in which the final randomly initialized k-means stage in traditional SSC is eliminated. We theoretically verify the correctness of the proposed method on noise-free data. Extensive experiments on synthetic and real-world data corrupted by noise demonstrate the robustness and effectiveness of the proposed method comparing to the well-established competitors.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124604054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early Warnings of Cyber Threats in Online Discussions 在线讨论中的网络威胁预警

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.94

Anna Sapienza, Alessandro Bessi, Saranya Damodaran, P. Shakarian, Kristina Lerman, Emilio Ferrara

引用次数: 67

Reducing False Positives of User-to-Entity First-Access Alerts for User Behavior Analytics 减少用户对实体首次访问警报的误报，用于用户行为分析

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.111

Baoming Tang, Qiaona Hu, Derek Lin

{"title":"Reducing False Positives of User-to-Entity First-Access Alerts for User Behavior Analytics","authors":"Baoming Tang, Qiaona Hu, Derek Lin","doi":"10.1109/ICDMW.2017.111","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.111","url":null,"abstract":"Detecting security threats from compromised account or malicious insider by leveraging enterprise traffic logs is the goal of user behavior-based analytics. For its ease of interpretation, a common analytic indicator used in the industry for user behavior analytics is whether a user accesses a network entity, such as a machine or process, for the first time. While this popular indicator does correlate well with the threat activities, it has the potential of generating volumes of false positives. This creates a problem for an analytic system of which the first-time access alerting capability is a part. We believe that the false positive rate from the indicator can be reduced by learning from users' historical entity access patterns and user context information. If the first-time access is expected, then its corresponding alert is suppressed. In this paper, we propose a user-to-entity prediction score which uses a recommender system for learning user data. In particular, we use factorization machines, along with necessary data normalization steps, to make predictions on real-world enterprise logs. We demonstrate this novel method is capable of reducing false positives of users' first-time entity access alerts in user behavior analytics applications.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128331869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

DAAD: A Framework for Detecting Agitation and Aggression in People Living with Dementia Using a Novel Multi-modal Sensor Network DAAD:一个使用新型多模态传感器网络检测痴呆症患者躁动和攻击的框架

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.98

Shehroz S. Khan, Tong Zhu, B. Ye, Alex Mihailidis, A. Iaboni, Kristine Newman, A. Wang, L. Martin

{"title":"DAAD: A Framework for Detecting Agitation and Aggression in People Living with Dementia Using a Novel Multi-modal Sensor Network","authors":"Shehroz S. Khan, Tong Zhu, B. Ye, Alex Mihailidis, A. Iaboni, Kristine Newman, A. Wang, L. Martin","doi":"10.1109/ICDMW.2017.98","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.98","url":null,"abstract":"With an increase in the population of older adults, the number of cases with dementia also increases. People living with dementia (PLwD) exhibit various behavioral and psychological experiences; agitation and aggression being the most common. Aggressive patients with dementia can harm themselves, other patients and the staff. In the past, researchers have used actigraphy to detect incidences of agitation and aggression in persons with dementia. However, actigraphy based solutions only consider body movement based parameters. In this paper, we present a novel multi-modal sensing framework currently being installed and tested at Toronto Rehabilitation Institute, Canada. This framework uses video cameras, wearable device (for both movement and physiological data), motion and door sensors, and pressure mats to collect various types of data that may be used to Detect and predict incidences of Agitation and Aggression in people with Dementia (DAAD). In this paper, we discuss the data collection, data processing and data fusion aspects using each of the sensors. Using the DAAD sensing platform, we present two pilot studies to demonstrate its effective functioning. We also discuss the challenges experienced with respect to ethics, hardware installation, software issues and data management.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129561590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Aggregation and Disaggregation of Information: A Holistic View 信息的聚合和分解:一个整体的观点

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.157

Yuyue Chen, Chuanren Liu

{"title":"Aggregation and Disaggregation of Information: A Holistic View","authors":"Yuyue Chen, Chuanren Liu","doi":"10.1109/ICDMW.2017.157","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.157","url":null,"abstract":"Data-driven analytics and decision-making have been essential for numerous applications in our society. To transform the data into a source of rich intelligence and support decision-making, data-driven analytics often need to aggregate intelligence from multiple sources and disaggregate signals into significant constituents. Though many existing approaches perform these two tasks respectively, there are few attempts to study them with a holistic view. This dissertation exploits the intrinsic connections between intelligence aggregation and signal disaggregation by developing novel models to capture and leverage various types of non-IIDness and inter-correlations in the data from complex systems. Our preliminary results show that, by identifying non-IIDness of information sources, our approach outperforms alternative methods for intelligence aggregation tasks. Also, by viewing disaggregation as the inverse function of aggregation and incorporating various types of inter-correlations in complex systems, we can also improve the performance for signal disaggregation tasks. Given these promising results, we will further improve the effectiveness and efficiency of our framework on large-scale data from different application fields.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132446814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Word Semantics Based 3-D Convolutional Neural Networks for News Recommendation 基于词语义的三维卷积神经网络新闻推荐

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.105

Vaibhav Kumar, Dhruv Khattar, Shashank Gupta, Vasudeva Varma

引用次数: 11

Entity Recommendation Via Integrating Multiple Types of Implicit Feedback in Heterogeneous Information Network 异构信息网络中集成多种隐式反馈的实体推荐

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.108

Xiaotong Suo, Fang Wei, K. Yu

引用次数: 1

Estimating Personality from Social Media Posts 从社交媒体帖子中估计个性

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.51

N. Alsadhan, D. Skillicorn

引用次数: 15