2014 IEEE International Conference on Data Mining Workshop最新文献_第3页

SUBSCALE: Fast and Scalable Subspace Clustering for High Dimensional Data SUBSCALE:高维数据的快速可伸缩子空间聚类

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.100

Amardeep Kaur, A. Datta

引用次数: 7

RepComment: A Fair Comment-Sentiment Representation System RepComment:一个公平的评论情感表达系统

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.22

Ting Wu, Chunxi Tan, Ming Cheung, P. Hui

引用次数: 0

Online Fusion of Incremental Learning for Wireless Sensor Networks 无线传感器网络增量学习的在线融合

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.79

H. H. W. J. Bosman, Giovanni Iacca, H. Wörtche, A. Liotta

{"title":"Online Fusion of Incremental Learning for Wireless Sensor Networks","authors":"H. H. W. J. Bosman, Giovanni Iacca, H. Wörtche, A. Liotta","doi":"10.1109/ICDMW.2014.79","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.79","url":null,"abstract":"Ever-more ubiquitous embedded systems provide us with large amounts of data. Performing analysis close to the data source allows for data reduction while giving information when unexpected behavior (i.e. Anomalies in the system under observation) occurs. This work presents a novel approach to online anomaly detection, based on an ensemble of classifiers that can be executed on distributed embedded systems. We consider both single and multi-dimensional input classifiers that are based on prediction errors. Predictions of single-dimensional time series input come from either a linear function model or general statistics over a data window. Multi-dimensional input stems from current and historical sensor values as well as predictions. We combine the classifier outputs in the ensemble using a heuristic method and Fisher's combined probability test. The proposed framework is tested thoroughly using synthetic and real-world data. The results are compared to known methods for anomaly detection on limited-resource systems. While individual classifiers perform comparably to known methods, our results show that using an ensemble of classifiers increases the overall detection of anomalies considerably.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125457589","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: 13

Latent Factor SVM for Text Categorization 文本分类的潜在因子支持向量机

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.9

Xiaofei Zhou, Li Guo, Ping Liu, Yanbing Liu

引用次数: 3

Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei 基于最小信息损失的多核学习布鲁氏锥虫鞭毛蛋白识别

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.142

Jim Jing-Yan Wang, Xin Gao

{"title":"Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei","authors":"Jim Jing-Yan Wang, Xin Gao","doi":"10.1109/ICDMW.2014.142","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.142","url":null,"abstract":"Trypanosma brucei (T. Brucei) is an important pathogen agent of African trypanosomiasis. The flagellum is an essential and multifunctional organelle of T. Brucei, thus it is very important to recognize the flagellar proteins from T. Brucei proteins for the purposes of both biological research and drug design. In this paper, we investigate computationally recognizing flagellar proteins in T. Brucei by pattern recognition methods. It is argued that an optimal decision function can be obtained as the difference of probability functions of flagella protein and the non-flagellar protein for the purpose of flagella protein recognition. We propose to learn a multi-kernel classification function to approximate this optimal decision function, by minimizing the information loss of such approximation which is measured by the Kull back - Leibler (KL) divergence. An iterative multi-kernel classifier learning algorithm is developed to minimize the KL divergence for the problem of T. Brucei flagella protein recognition, experiments show its advantage over other T. Brucei flagellar protein recognition and multi-kernel learning methods.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121959940","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

Clustering and Classification of Like-Minded People from their Tweets 从他们的推文中聚类和分类志同道合的人

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.161

S. Jaffali, Salma Jamoussi, A. B. Hamadou, K. Smaïli

引用次数: 4

Segmentation and Splitting of Touching Vaginal Bacteria Based on Superpixel and Effective Distance 基于超像素和有效距离的触摸阴道细菌分割与分裂

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.172

Youyi Song, Dong Ni, Liang He, Siping Chen, B. Lei, Tianfu Wang

引用次数: 1

A New Fast Minimum Spanning Tree-Based Clustering Technique 一种新的快速最小生成树聚类技术

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.139

Xiaochun Wang, X. Wang, Jihua Zhu

{"title":"A New Fast Minimum Spanning Tree-Based Clustering Technique","authors":"Xiaochun Wang, X. Wang, Jihua Zhu","doi":"10.1109/ICDMW.2014.139","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.139","url":null,"abstract":"Due to its important applications in data mining, many techniques have been developed for clustering. For today's real-world databases which typically have millions of items with many thousands of fields, resulting in datasets that range in size into terabytes, many traditional clustering techniques have more and more restricted capabilities and novel approaches that are computationally efficient have become more and more popular. In this paper, a new efficient approach to graph-theoretical clustering using a minimum spanning tree representation of a dataset is proposed which consists of two-phases. In the first phase, we modify the standard Prim's algorithm in such a way that an efficient construction of such a tree can be realized based on k-nearest neighbor search mechanisms, during which a new edge weight is defined to maximize the intra-cluster similarity and minimize the inter-cluster similarity of the data set. In the second phase, based on the intuition that the data points are closer in the same cluster than in different clusters, the longest edges in the minimum spanning tree obtained from the first phase are removed to form clusters as the standard minimum spanning tree-based clustering algorithms do. Experiments on synthetic as well as real data sets have been conducted to show that our proposed approach works well with respect to the state-of-the-art methods.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115066230","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: 6

Semi-Supervised Method for Multi-category Emotion Recognition in Tweets 推文多类别情感识别的半监督方法

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.146

Valentina Sintsova, C. Musat, P. Pu

{"title":"Semi-Supervised Method for Multi-category Emotion Recognition in Tweets","authors":"Valentina Sintsova, C. Musat, P. Pu","doi":"10.1109/ICDMW.2014.146","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.146","url":null,"abstract":"Each tweet is limited to 140 characters. This constraint surprisingly makes Twitter a more spontaneous platform to express our emotions. Detecting emotions and correctly classifying them automatically is an increasingly important task if we want to understand how large groups of people feel about an event or relevant topic. However, constructing supervised classifiers can be a daunting task because of the high manual annotation costs. We propose constructing emotion classifiers with a minimal amount of initial knowledge (e.g. A general-purpose emotion lexicon) and using a semi-supervised learning method to extend it to correctly detect more emotional tweets within a specific domain. Additionally, we show that our algorithm, Balanced Weighted Voting (or BWV) is able to overcome the imbalanced distribution of emotions in the initial labeled data. Our validation experiments show that BWV improves the performance of three initial classifiers, at least in the specific domain of sports. Furthermore, its comparison with other two learning strategies reveals its superiority in terms of macro F1-score, as well as more stable performance among different emotion categories.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"334 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116462553","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: 9

NaviSoc: A Socially Enhanced Real-Time Navigator NaviSoc:社交增强的实时导航器

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.77

Nikolaos Louloudakis, Vasileios Theodosiadis, H. Kondylakis, K. Stefanidis

引用次数: 1