KDD Tutorial Home Info. Abstract Outline Tutors Slides

Information.


KDD 2018 Website
Time: 1:00 PM - 5:00 PM
Location: ICC Capital Suite Room 17 (Level 3)
Slides

Abstract.


Many data mining tasks cannot be completely addressed by automated processes, such as sentiment analysis and image classification. Crowdsourcing is an effective way to harness the human cognitive ability to process these machine-hard tasks. Thanks to public crowdsourcing platforms, e.g., Amazon Mechanical Turk and CrowdFlower, we can easily involve hundreds of thousands of ordinnary workers (i.e., the crowd) to address these machine-hard tasks. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowd-powered data mining. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data mining. Next we review crowd-powered data mining operations, including classification, clustering, pattern mining, machine learning using the crowd (including deep learning, tansfer learning and semi-supervised learning) and knowledge discovery. Finally, we provide the emerging challenges in crowdsourced data mining.

Outline.


1. Crowdsourcing Overview (20 minutes)

2. Quality control (40 minutes)

3. Cost control (40 minutes)

Break (30 minutes)

4. Latency control (20 minutes)

5. Crowd Mining (60 minutes)

6. Challenges (10 minutes)

Tutors.


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Guoliang Li

Associate Professor

Tsinghua University, China

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Jiannan Wang

Assistant Professor

Simon Fraser University, CA

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Ju Fan

Associate Professor

Renmin University, China

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Yudian Zheng

Researcher

Twitter, USA

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Chengliang Chai

Ph.D.

Tsinghua University, China

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Slides.


Slides

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