Synthesis Lectures on Data Mining and Knowledge Discovery最新文献

Correlation Clustering 关联聚类

Synthesis Lectures on Data Mining and Knowledge Discovery Pub Date : 2022-03-08 DOI: 10.2200/s01163ed1v01y202201dmk019

F. Bonchi, David García-Soriano, Francesco Gullo

引用次数: 0

Detecting Fake News on Social Media 发现社交媒体上的假新闻

Synthesis Lectures on Data Mining and Knowledge Discovery Pub Date : 2019-07-03 DOI: 10.2200/s00926ed1v01y201906dmk018

Kai Shu, Huan Liu

引用次数: 61

Multidimensional Mining of Massive Text Data 海量文本数据的多维挖掘

Synthesis Lectures on Data Mining and Knowledge Discovery Pub Date : 2019-03-21 DOI: 10.2200/S00903ED1V01Y201902DMK017

Chao Zhang, Jiawei Han

引用次数: 8

Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems 利用群体差异的力量:使用模式来解决数据分析问题

Synthesis Lectures on Data Mining and Knowledge Discovery Pub Date : 2019-02-22 DOI: 10.2200/S00897ED1V01Y201901DMK016

Guozhu Dong

引用次数: 5

Mining Latent Entity Structures 挖掘潜在实体结构

Synthesis Lectures on Data Mining and Knowledge Discovery Pub Date : 2015-03-12 DOI: 10.2200/S00625ED1V01Y201502DMK010

Chi Wang, Jiawei Han

{"title":"Mining Latent Entity Structures","authors":"Chi Wang, Jiawei Han","doi":"10.2200/S00625ED1V01Y201502DMK010","DOIUrl":"https://doi.org/10.2200/S00625ED1V01Y201502DMK010","url":null,"abstract":"The \"big data\" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3) entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions.","PeriodicalId":124124,"journal":{"name":"Synthesis Lectures on Data Mining and Knowledge Discovery","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132055488","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

Provenance Data in Social Media 社交媒体中的来源数据

Synthesis Lectures on Data Mining and Knowledge Discovery Pub Date : 2013-05-01 DOI: 10.2200/s00496ed1v01y201304dmk007

Geoffrey Barbier, Zhuo Feng, Pritam Gundecha, Huan Liu

{"title":"Provenance Data in Social Media","authors":"Geoffrey Barbier, Zhuo Feng, Pritam Gundecha, Huan Liu","doi":"10.2200/s00496ed1v01y201304dmk007","DOIUrl":"https://doi.org/10.2200/s00496ed1v01y201304dmk007","url":null,"abstract":"Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media. Table of Contents: Information Provenance in Social Media / Provenance Attributes / Provenance via Network Information / Provenance Data","PeriodicalId":124124,"journal":{"name":"Synthesis Lectures on Data Mining and Knowledge Discovery","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134081313","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: 50

Advances in Graph Neural Networks 图神经网络研究进展

Synthesis Lectures on Data Mining and Knowledge Discovery Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-16174-2

Chuan Shi, Xiao Wang, Cheng Yang

引用次数: 0