2018 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第7页

A Multi-granular Hierarchical Evaluation Model for Multiple Criteria Three Sorting 多准则三排序的多粒度分级评价模型

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00078

Hong Yu, Zuoyu Sun, Guoyin Wang, Jie Li, Yongfang Xie, Gang Guo

{"title":"A Multi-granular Hierarchical Evaluation Model for Multiple Criteria Three Sorting","authors":"Hong Yu, Zuoyu Sun, Guoyin Wang, Jie Li, Yongfang Xie, Gang Guo","doi":"10.1109/ICDMW.2018.00078","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00078","url":null,"abstract":"A hierarchical organization is common in most corporations, where some entities (objects) in the lower granulation level are subordinate to a single other entity (object) in the follow-on higher granulation level. During recent years, how to evaluate the objects in a group corporation has become a key strategic consideration. Therefore, this paper proposes a multi-granular hierarchical evaluation model for multiple criteria three sorting. In the model, the objects in a granulation level are assigned to three ordinal classes based on the theory of three-way decisions and the rating system, instead of accurately sorting them. Furthermore, the evaluation results from the previous granulation level are employed as one type of criteria in the follow-on granulation level, called the evaluation result criteria. Additionally, we propose a three-way TOPSIS (the Technique for Order of Preference by Similarity to Ideal Solution) method based on the characteristics of four types of criteria such as evaluation result criteria, benefit criteria, cost criteria and non-monotonic criteria. Besides, a pre-evaluation strategy is devised at each level inspired by the concept of \"first impressions\". Then, the final evaluation results are obtained by studying the qualitative evaluation results of pre-evaluation method and the quantitative evaluation results of three-way TOPSIS method. Finally, a numerical case study from an aluminium electrolysis production enterprise is illustrated to show its viability.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123414848","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: 1

Review of Graph Processing Frameworks 图处理框架回顾

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00144

Siddharth Bhatia, Rajiv Kumar

引用次数: 2

Sentiment Prediction in Social Networks 社交网络中的情感预测

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00190

Shengmin Jin, R. Zafarani

{"title":"Sentiment Prediction in Social Networks","authors":"Shengmin Jin, R. Zafarani","doi":"10.1109/ICDMW.2018.00190","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00190","url":null,"abstract":"Sentiment analysis research has focused on using text for predicting sentiments without considering the unavoidable peer influence on user emotions and opinions. The lack of large-scale ground-truth data on sentiments of users in social networks has limited research on how predictable sentiments are from social ties. In this paper, using a large-scale dataset on human sentiments, we study sentiment prediction within social networks. We demonstrate that sentiments are predictable using structural properties of social networks alone. With social science and psychology literature, we provide evidence on sentiments being connected to social relationships at four different network levels, starting from the ego-network level and moving up to the whole-network level. We discuss emotional signals that can be captured at each level of social relationships and investigate the importance of structural features on each network levels. We demonstrate that sentiment prediction that solely relies on social network structure can be as (or more) accurate than text-based techniques. For the situations where complete posts and friendship information are difficult to get, we analyze the trade-off between the sentiment prediction performance and the available information. When computational resources are limited, we show that using only four network properties, one can predict sentiments with competitive accuracy. Our findings can be used to (1) validate the peer influence on user sentiments, (2) improve classical text-based sentiment prediction methods, (3) enhance friend recommendation by utilizing sentiments, and (4) help identify personality traits.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130383965","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: 4

Functional Brain Areas Mapping in Patients with Glioma Based on Resting-State fMRI Data Decomposition 基于静息状态fMRI数据分解的脑胶质瘤患者功能脑区定位

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00049

M. Sharaev, A. Smirnov, T. Melnikova-Pitskhelauri, V. Orlov, Evgeny Burnaev, I. Pronin, D. Pitskhelauri, A. Bernstein

{"title":"Functional Brain Areas Mapping in Patients with Glioma Based on Resting-State fMRI Data Decomposition","authors":"M. Sharaev, A. Smirnov, T. Melnikova-Pitskhelauri, V. Orlov, Evgeny Burnaev, I. Pronin, D. Pitskhelauri, A. Bernstein","doi":"10.1109/ICDMW.2018.00049","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00049","url":null,"abstract":"In current work we propose a three-step approach to automatic and efficient functional brain areas mapping as well demonstrate in case studies on three patients with gliomas the potential applicability of constrained source separation technique (semiblind Independent Component Analysis, ICA) to brain networks discovery and the similarity of task-based-fMRI (t-fMRI) and resting state-fMRI (rs-fMRI) results. Blind and semiblind ICA-analysis was applied for both methods t-fMRI and rs-fMRI. To measure similarity between spatial maps we used Dice coefficient, which shows the ratio of overlapping voxels and all active voxels in two compared maps for each patient Based on the analysis of Dice coefficients, there was a fairly high degree of overlap between the t-fMRI active areas, Broca and Wernicke and the language network obtained from rs-fMRI. The degree of motor areas overlap with sensorimotor network is less pronounced, but the activation sites correspond to anatomical landmarks - a complex of central gyri and supplementary motor area. In general, in comparisons of the functional brain areas obtained with t-fMRI and rs-fMRI, there is a greater specificity of semiblind ICA compared to blind ICA. RSNs of interest (motor and language) discovered by rs-fMRI highly correlate with t-fMRI reference and are located in anticipated anatomical regions. As a result, rs-fMRI maps seem as a good approximation of t-fMRI maps, especially in case of semiblind ICA decomposition. We hope that further our research of individual changes in sensorimotor and language networks based on functional rs-MRI will allow predicting the activity of neural network architectures and non-invasive mapping of functional areas for preoperative planning.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122297560","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: 3

Event Detection in Twitter: A Keyword Volume Approach Twitter中的事件检测:一种关键字量方法

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00172

A. Hossny, Lewis Mitchell

{"title":"Event Detection in Twitter: A Keyword Volume Approach","authors":"A. Hossny, Lewis Mitchell","doi":"10.1109/ICDMW.2018.00172","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00172","url":null,"abstract":"Event detection using social media streams needs a set of informative features with strong signals that need minimal preprocessing and are highly associated with events of interest. Identifying these informative features as keywords from Twitter is challenging, as people use informal language to express their thoughts and feelings. This informality includes acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests. The volume of these keywords is tracked in real time to identify the events of interest in a binary classification scheme. We use keywords within word-pairs to capture the context. The proposed method is to binarize vectors of daily counts for each word-pair by applying a spike detection temporal filter, then use the Jaccard metric to measure the similarity of the binary vector for each word-pair with the binary vector describing event occurrence. The top n word-pairs are used as features to classify any day to be an event or non-event day. The selected features are tested using multiple classifiers such as Naive Bayes, SVM, Logistic Regression, KNN and decision trees. They all produced AUC ROC scores up to 0.91 and F1 scores up to 0.79. The experiment is performed using the English language in multiple cities such as Melbourne, Sydney and Brisbane as well as the Indonesian language in Jakarta. The two experiments, comprising different languages and locations, yielded similar results.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116430382","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: 23

Geofences on the Blockchain: Enabling Decentralized Location-Based Services 区块链上的地理围栏:实现分散的基于位置的服务

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00021

Friedhelm Victor, Sebastian Zickau

{"title":"Geofences on the Blockchain: Enabling Decentralized Location-Based Services","authors":"Friedhelm Victor, Sebastian Zickau","doi":"10.1109/ICDMW.2018.00021","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00021","url":null,"abstract":"A decentralized ride-or carsharing application is among the early proposals of what smart contracts on blockchains may enable in the future. To facilitate use cases in the field of location-based services (LBS), smart contracts need to receive trustworthy positioning information, and be able to process them. We propose an approach on how geofences can be defined in smart contracts, and how supplied positions can be evaluated on whether they are contained in the geofence or not. The approach relies on existing location encoding systems like Geohashes and S2 cells that can transform polygons into a grid of cells. These can be stored in a smart contract to represent a geofence. An oracle run by a mobile network provider can submit network-based positioning information to the contract, that compares it with the geofence. We evaluate the location encoding systems on their ability to model city geofences and mobile network cell position estimates and analyze the costs associated with storing and evaluating received oracle-positions in an Ethereum-based smart contract implementation. Our results show that S2 encodings perform better than Geohashes, that the one-time cost of geofence definition corresponds linearly with the number of grid cells used, and that the evaluation of oracle-submitted locations does not incur high costs.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130893336","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: 17

Interpreting Traffic Congestion Using Fundamental Diagrams and Probabilistic Graphical Modeling 用基本图和概率图形模型解释交通拥堵

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00090

Carla Silva, P. D’orey, Ana Aguiar

引用次数: 4

NEARM: Natural Language Enhanced Association Rules Mining NEARM:自然语言增强关联规则挖掘

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00071

Shiya Ren, Zhixing Li, Huaming Wang, Yuan Li, Ke Shen, Sijie Cheng

{"title":"NEARM: Natural Language Enhanced Association Rules Mining","authors":"Shiya Ren, Zhixing Li, Huaming Wang, Yuan Li, Ke Shen, Sijie Cheng","doi":"10.1109/ICDMW.2018.00071","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00071","url":null,"abstract":"Knowledge bases(KBs), which are typical heterogeneous graphs that contain numerous triple facts of various types and relations, have shown remarkable advantages in many natural language processing(NLP) tasks. KBs usually integrate information from different sources such as human-edited online encyclopedias, news articles and even social networks. Due to the heterogeneous nature of these sources, both the KBs themselves and their applications on NLP tasks are far from perfect. On the one hand, KBs need further completion and refining to cover more knowledge with higher qualities. On the other hand, the joint modeling of structured knowledge in KBs and unstructured texts have not been well investigated. This paper proposes a novel natural language enhanced association rules mining (NEARM) framework to improve KBs. NEARM finds knowledge fragments from free texts in a data-driven manner. It first groups raw data (sentences) which contains related entity pairs into clusters of different granularities, and then integrates them with facts from KBs to mine rules in each clusters. To capture the relations between plain text and triple facts, NEARM produces rules that contain natural language patterns and/or triple facts in antecedent, and triple facts in consequent. In this way, NEARM can infer triple facts directly from plain text. At last, experiment results demonstrate the effectiveness of the NEARM on relation classification and triple facts reasoning.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131023500","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: 2

Hubness as a Case of Technical Algorithmic Bias in Music Recommendation Hubness:音乐推荐中技术算法偏差的一个案例

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00154

A. Flexer, M. Dörfler, Jan Schlüter, Thomas Grill

引用次数: 5

River: A Real-Time Influence Monitoring System on Social Media Streams River:社交媒体流的实时影响监测系统

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00203

M. Sha, Yuchen Li, Yanhao Wang, Wentian Guo, K. Tan

引用次数: 4