SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining最新文献_第6页

Contextual crowd intelligence 情境群体智能

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-09-25 DOI: 10.1145/2674026.2674032

B. Ooi, K. Tan, Quoc Trung Tran, J. Yip, Gang Chen, Zheng Jye Ling, Thi Nguyen, A. Tung, Meihui Zhang

引用次数: 15

Open challenges for data stream mining research 数据流挖掘研究的开放挑战

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-09-25 DOI: 10.1145/2674026.2674028

G. Krempl, I. Žliobaitė, D. Brzezinski, E. Hüllermeier, Mark Last, V. Lemaire, T. Noack, Ammar Shaker, S. Sievi, M. Spiliopoulou, J. Stefanowski

{"title":"Open challenges for data stream mining research","authors":"G. Krempl, I. Žliobaitė, D. Brzezinski, E. Hüllermeier, Mark Last, V. Lemaire, T. Noack, Ammar Shaker, S. Sievi, M. Spiliopoulou, J. Stefanowski","doi":"10.1145/2674026.2674028","DOIUrl":"https://doi.org/10.1145/2674026.2674028","url":null,"abstract":"Every day, huge volumes of sensory, transactional, and web data are continuously generated as streams, which need to be analyzed online as they arrive. Streaming data can be considered as one of the main sources of what is called big data. While predictive modeling for data streams and big data have received a lot of attention over the last decade, many research approaches are typically designed for well-behaved controlled problem settings, overlooking important challenges imposed by real-world applications. This article presents a discussion on eight open challenges for data stream mining. Our goal is to identify gaps between current research and meaningful applications, highlight open problems, and define new application-relevant research directions for data stream mining. The identified challenges cover the full cycle of knowledge discovery and involve such problems as: protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms. The resulting analysis is illustrated by practical applications and provides general suggestions concerning lines of future research in data stream mining.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"1 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2014-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91212297","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: 267

Interview: Michael Brodie, leading database researcher, industry leader, thinker 访谈:Michael Brodie，著名数据库研究员，行业领袖，思想家

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-09-25 DOI: 10.1145/2674026.2674035

Gregory Piatetsky

引用次数: 0

Mining text and social streams: a review 挖掘文本和社交流:综述

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-06-16 DOI: 10.1145/2641190.2641194

C. Aggarwal

引用次数: 30

Brain network analysis: a data mining perspective 大脑网络分析:数据挖掘的视角

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-06-16 DOI: 10.1145/2641190.2641196

Xiangnan Kong, Philip S. Yu

引用次数: 45

Predictive analysis of engine health for decision support 用于决策支持的发动机运行状况预测分析

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-06-16 DOI: 10.1145/2641190.2641197

Shubhabrata Mukherjee, A. Varde, G. Javidi, E. Sheybani

{"title":"Predictive analysis of engine health for decision support","authors":"Shubhabrata Mukherjee, A. Varde, G. Javidi, E. Sheybani","doi":"10.1145/2641190.2641197","DOIUrl":"https://doi.org/10.1145/2641190.2641197","url":null,"abstract":"Data mining, the discovery of knowledge from data, bridges several disciplines such as database management, artificial intelligence, statistics, visualization and the domain of the data, e.g., biology or engineering. Knowledge discovered by mining the data can be used for various purposes such as developing decision support systems and intelligent tutors. In this paper we present such a data mining problem in the mechanical engineering domain where knowledge discovery from the data is performed using statistical approaches, to conduct predictive analysis for decision support. More specifically, we focus on the engine health problem which consists of using existing data on the behavior of an engine in order to predict whether the engine is capable of functioning well (i.e., it is healthy) and to offer suggestions on preventive maintenance. The data we use for this predictive analysis consists of graphs that plot process parameters such as the vibration and temperature of the engine with respect to time. In this paper we define the problem in detail, propose a solution based on statistical inference techniques, summarize our experimental evaluation and discuss the applications of this work in various fields from a decision support angle.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"21 1","pages":"39-49"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84933008","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

Mining social media with social theories: a survey 用社会理论挖掘社交媒体:一项调查

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-06-16 DOI: 10.1145/2641190.2641195

Jiliang Tang, Yi Chang, Huan Liu

{"title":"Mining social media with social theories: a survey","authors":"Jiliang Tang, Yi Chang, Huan Liu","doi":"10.1145/2641190.2641195","DOIUrl":"https://doi.org/10.1145/2641190.2641195","url":null,"abstract":"The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social media data is big, linked, noisy, highly unstructured and in- complete, and differs from data in traditional data mining, which cultivates a new research field - social media mining. Social theories from social sciences are helpful to explain social phenomena. The scale and properties of social media data are very different from these of data social sciences use to develop social theories. As a new type of social data, social media data has a fundamental question - can we apply social theories to social media data? Recent advances in computer science provide necessary computational tools and techniques for us to verify social theories on large-scale social media data. Social theories have been applied to mining social media. In this article, we review some key social theories in mining social media, their verification approaches, interesting findings, and state-of-the-art algorithms. We also discuss some future directions in this active area of mining social media with social theories.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"1155 1","pages":"20-29"},"PeriodicalIF":0.0,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91201947","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: 138

Clustering high dimensional data: examining differences and commonalities between subspace clustering and text clustering - a position paper 聚类高维数据:检查子空间聚类和文本聚类之间的差异和共性-立场文件

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-06-16 DOI: 10.1145/2641190.2641192

H. Kriegel, Eirini Ntoutsi

引用次数: 6

Comprehensible classification models: a position paper 可理解的分类模型:立场文件

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-03-17 DOI: 10.1145/2594473.2594475

A. Freitas

引用次数: 532

Ensembles for unsupervised outlier detection: challenges and research questions a position paper 无监督异常值检测的集成:挑战和研究问题

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2014-03-17 DOI: 10.1145/2594473.2594476

A. Zimek, R. Campello, J. Sander

引用次数: 245