2010 IEEE International Conference on Data Mining Workshops最新文献_第10页

Integer Programming for Multi-class Active Learning 多类主动学习的整数规划

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.148

Dragomir Yankov, Suju Rajan, A. Ratnaparkhi

引用次数: 1

Enhancing Document Exploration with OLAP 使用OLAP增强文档探索

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.37

Zhibo Chen, Carlos Garcia-Alvarado, C. Ordonez

{"title":"Enhancing Document Exploration with OLAP","authors":"Zhibo Chen, Carlos Garcia-Alvarado, C. Ordonez","doi":"10.1109/ICDMW.2010.37","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.37","url":null,"abstract":"Finding relevant documents in digital libraries has been a well studied problem in information retrieval. It is not uncommon to see users browsing digital collections without having a clear idea of the keyword search that they should perform. However, we believe that such initial query search is not totally independent from the target search. Therefore, we use these initial document selections to further explore these documents. In the following demonstration, we exploit On-line Analytical Processing (OLAP) for knowledge discovery in digital collections to achieve query refinement. Such refinement is the result of applying a traditional ranking technique, based on the vector space model, selecting the top keywords in the resulting subset of documents, and then displaying certain cuboids of the keywords. Based on these cuboids, which are ranked by their frequency, the users can select a query that can better represent their actual target search. We show that this document exploration can be done efficiently within the DBMS and exploit in-database extensions, such as User-Defined Functions, as well as standard SQL. Additionally, we demonstrate a novel approach to obtaining query refinement through OLAP data cubes.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126901236","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

A Block Mixture Model for Pattern Discovery in Preference Data 偏好数据模式发现的块混合模型

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.59

Nicola Barbieri, M. Guarascio, G. Manco

引用次数: 5

On Attribute Disclosure in Randomization Based Privacy Preserving Data Publishing 基于随机化的隐私保护数据发布中的属性披露研究

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.76

Ling Guo, Xiaowei Ying, Xintao Wu

引用次数: 9

System Biology Approach for Elucidating the Relationship Between Indonesian Herbal Plants and the Efficacy of Jamu 用系统生物学方法研究印尼草本植物与Jamu功效的关系

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.105

F. Afendi, L. K. Darusman, Aki Hirai, M. Altaf-Ul-Amin, Hiroki Takahashi, Kensuke Nakamura, S. Kanaya

引用次数: 15

A Framework for Emotion Mining from Text in Online Social Networks 在线社交网络中文本情感挖掘的框架

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.75

Mohamed Yassine, Hazem M. Hajj

{"title":"A Framework for Emotion Mining from Text in Online Social Networks","authors":"Mohamed Yassine, Hazem M. Hajj","doi":"10.1109/ICDMW.2010.75","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.75","url":null,"abstract":"Online Social Networks are so popular nowadays that they are a major component of an individual’s social interaction. They are also emotionally-rich environments where close friends share their emotions, feelings and thoughts. In this paper, a new framework is proposed for characterizing emotional interactions in social networks, and then using these characteristics to distinguish friends from acquaintances. The goal is to extract the emotional content of texts in online social networks. The interest is in whether the text is an expression of the writer’s emotions or not. For this purpose, text mining techniques are performed on comments retrieved from a social network. The framework includes a model for data collection, database schemas, data processing and data mining steps. The informal language of online social networks is a main point to consider before performing any text mining techniques. This is why the framework includes the development of special lexicons. In general, the paper presents a new perspective for studying friendship relations and emotions’ expression in online social networks where it deals with the nature of these sites and the nature of the language used. It considers Lebanese Face book users as a case study. The technique adopted is unsupervised, it mainly uses the k-means clustering algorithm. Experiments show high accuracy for the model in both determining subjectivity of texts and predicting friendship.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128866960","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: 105

QueRIE: A Query Recommender System Supporting Interactive Database Exploration QueRIE:一个支持交互式数据库探索的查询推荐系统

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.43

Sarika Mittal, Jothi Swarubini Vindhiya Varman, Gloria Chatzopoulou, M. Eirinaki, N. Polyzotis

引用次数: 18

Large-Scale Customized Models for Advertisers 广告主大规模定制模式

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.157

A. Bagherjeiran, A. O. Hatch, A. Ratnaparkhi, R. Parekh

{"title":"Large-Scale Customized Models for Advertisers","authors":"A. Bagherjeiran, A. O. Hatch, A. Ratnaparkhi, R. Parekh","doi":"10.1109/ICDMW.2010.157","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.157","url":null,"abstract":"Performance advertisers want to maximize the return on their advertising spend. In the online advertising world, this means showing the ad only to those users most likely to convert i.e. buy a product or service. Existing ad targeting solutions such as context targeting and rule-based segment targeting primarily leverage marketing intuition to identify audience segments that would be likely to convert. Even the more sophisticated model-based approaches such as behavioral targeting identify audience segments interested in certain coarse-grained categories defined by the publisher. Advertisers are now able, through beaconing, to tell us exactly who their preferred customers are. Advertisers want to augment their existing advertising campaign with custom models that learn from the campaign and focus on attracting new users. Motivated by our experience with advertisers, we pose this problem within the context of ensemble learning. Building custom models for an existing ad campaign can be viewed as operations on an ensemble classifier: add, modify, or complement a classifier. An ideal new classifier should incrementally improve the ensemble and minimize overlap with any existing classifiers already in the ensemble–it should learn something new. With the proposed approach we are able to augment the advertising campaigns of several large advertisers at a large online advertising company.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121777318","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: 14

RnR: Extracting Rationale from Online Reviews and Ratings RnR:从在线评论和评级中提取基本原理

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.167

Dwi A. P. Rahayu, S. Krishnaswamy, O. Alahakoon, C. Labbé

引用次数: 9

S4: Distributed Stream Computing Platform S4:分布式流计算平台

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.172

L. Neumeyer, B. Robbins, Anish Nair, Anand Kesari

引用次数: 968